Color Fourier single-pixel imaging(FSI)enables efficient spectral and spatial imaging.Here,we propose a Fourier single-pixel imaging scheme with a random color filter array(FSI-RCFA).The proposed method employs a rand...Color Fourier single-pixel imaging(FSI)enables efficient spectral and spatial imaging.Here,we propose a Fourier single-pixel imaging scheme with a random color filter array(FSI-RCFA).The proposed method employs a random color filter array(RCFA)to modulate Fourier patterns.A three-step phase-shifting technique reconstructs the Fourier spectrum,followed by an RCFA-based demosaicing algorithm to recover color images.Compared to traditional color FSI based on Bayer color filter array schemes(FSI-BCFA),our approach achieves superior separation between chrominance and luminance components in the frequency domain.Simulation results demonstrate that the FSI-RCFA method achieves a lower mean squared error(MSE),a higher peak signal-to-noise ratio(PSNR),and superior noise resistance compared to FSI-BCFA,while enabling direct single-channel pixel measurements for targeted applications such as agricultural defect detection.展开更多
Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ul...Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ultra-low sampling rates.We develop an alternative optimization with physics and a data-driven diffusion network(APD-Net).It features alternative optimization driven by the learned task-agnostic natural image prior and the task-specific physics prior.During the training stage,APD-Net harnesses the power of diffusion models to capture data-driven statistics of natural signals.In the inference stage,the physics prior is introduced as corrective guidance to ensure consistency between the physics imaging model and the natural image probability distribution.Through alternative optimization,APD-Net reconstructs data-efficient,high-fidelity images that are statistically and physically compliant.To accelerate reconstruction,initializing images with the inverse SPI physical model reduces the need for reconstruction inference from 100 to 30 steps.Through both numerical simulations and real prototype experiments,APD-Net achieves high-quality,full-color reconstructions of complex natural images at a low sampling rate of 1%.In addition,APD-Net’s tuning-free nature ensures robustness across various imaging setups and sampling rates.Our research offers a broadly applicable approach for various applications,including but not limited to medical imaging and industrial inspection.展开更多
Existing single-pixel imaging(SPI)and sensing techniques suffer from poor reconstruction quality and heavy computation cost,limiting their widespread application.To tackle these challenges,we propose a large-scale sin...Existing single-pixel imaging(SPI)and sensing techniques suffer from poor reconstruction quality and heavy computation cost,limiting their widespread application.To tackle these challenges,we propose a large-scale single-pixel imaging and sensing(SPIS)technique that enables high-quality megapixel SPI and highly efficient image-free sensing with a low sampling rate.Specifically,we first scan and sample the entire scene using small-size optimized patterns to obtain information-coupled measurements.Compared with the conventional full-sized patterns,small-sized optimized patterns achieve higher imaging fidelity and sensing accuracy with 1 order of magnitude fewer pattern parameters.Next,the coupled measurements are processed through a transformer-based encoder to extract high-dimensional features,followed by a task-specific plugand-play decoder for imaging or image-free sensing.Considering that the regions with rich textures and edges are more difficult to reconstruct,we use an uncertainty-driven self-adaptive loss function to reinforce the network’s attention to these regions,thereby improving the imaging and sensing performance.Extensive experiments demonstrate that the reported technique achieves 24.13 dB megapixel SPI at a sampling rate of 3%within 1 s.In terms of sensing,it outperforms existing methods by 12%on image-free segmentation accuracy and achieves state-of-the-art image-free object detection accuracy with an order of magnitude less data bandwidth.展开更多
Single-pixel imaging(SPI)is a prominent scattering media imaging technique that allows image transmission via one-dimensional detection under structured illumination,with applications spanning from long-range imaging ...Single-pixel imaging(SPI)is a prominent scattering media imaging technique that allows image transmission via one-dimensional detection under structured illumination,with applications spanning from long-range imaging to microscopy.Recent advancements leveraging deep learning(DL)have significantly improved SPI performance,especially at low compression ratios.However,most DL-based SPI methods proposed so far rely heavily on extensive labeled datasets for supervised training,which are often impractical in real-world scenarios.Here,we propose an unsupervised learningenabled label-free SPI method for resilient information transmission through unknown dynamic scattering media.Additionally,we introduce a physics-informed autoencoder framework to optimize encoding schemes,further enhancing image quality at low compression ratios.Simulation and experimental results demonstrate that high-efficiency data transmission with structural similarity exceeding 0.9 is achieved through challenging turbulent channels.Moreover,experiments demonstrate that in a 5 m underwater dynamic turbulent channel,USAF target imaging quality surpasses traditional methods by over 13 dB.The compressive encoded transmission of 720×720 resolution video exceeding 30 seconds with great fidelity is also successfully demonstrated.These preliminary results suggest that our proposed method opens up a new paradigm for resilient information transmission through unknown dynamic scattering media and holds potential for broader applications within many other scattering media imaging technologies.展开更多
In recent years,deep learning has been introduced into the field of Single-pixel imaging(SPI),garnering significant attention.However,conventional networks still exhibit limitations in preserving image details.To addr...In recent years,deep learning has been introduced into the field of Single-pixel imaging(SPI),garnering significant attention.However,conventional networks still exhibit limitations in preserving image details.To address this issue,we integrate Large Kernel Convolution(LKconv)into the U-Net framework,proposing an enhanced network structure named U-LKconv network,which significantly enhances the capability to recover image details even under low sampling conditions.展开更多
We propose a method of complex-amplitude Fourier single-pixel imaging(CFSI)with coherent structured illumination to acquire both the amplitude and phase of an object.In the proposed method,an object is illustrated by ...We propose a method of complex-amplitude Fourier single-pixel imaging(CFSI)with coherent structured illumination to acquire both the amplitude and phase of an object.In the proposed method,an object is illustrated by a series of coherent structured light fields,which are generated by a phase-only spatial light modulator,the complex Fourier spectrum of the object can be acquired sequentially by a single-pixel photodetector.Then the desired complex-amplitude image can be retrieved directly by applying an inverse Fourier transform.We experimentally implemented this CFSI with several different types of objects.The experimental results show that the proposed method provides a promising complex-amplitude imaging approach with high quality and a stable configuration.Thus,it might find broad applications in optical metrology and biomedical science.展开更多
Single-pixel imaging(SPI)can transform 2D or 3D image data into 1D light signals,which offers promising prospects for image compression and transmission.However,during data communication these light signals in public ...Single-pixel imaging(SPI)can transform 2D or 3D image data into 1D light signals,which offers promising prospects for image compression and transmission.However,during data communication these light signals in public channels will easily draw the attention of eavesdroppers.Here,we introduce an efficient encryption method for SPI data transmission that uses the 3D Arnold transformation to directly disrupt 1D single-pixel light signals and utilizes the elliptic curve encryption algorithm for key transmission.This encryption scheme immediately employs Hadamard patterns to illuminate the scene and then utilizes the 3D Arnold transformation to permutate the 1D light signal of single-pixel detection.Then the transformation parameters serve as the secret key,while the security of key exchange is guaranteed by an elliptic curve-based key exchange mechanism.Compared with existing encryption schemes,both computer simulations and optical experiments have been conducted to demonstrate that the proposed technique not only enhances the security of encryption but also eliminates the need for complicated pattern scrambling rules.Additionally,this approach solves the problem of secure key transmission,thus ensuring the security of information and the quality of the decrypted images.展开更多
We propose a single-pixel imaging(SPI)method to achieve a higher-resolution image via the Hadamard transform matrix.Unlike traditional SPI schemes,this new method recovers images by correlating single-pixel signals wi...We propose a single-pixel imaging(SPI)method to achieve a higher-resolution image via the Hadamard transform matrix.Unlike traditional SPI schemes,this new method recovers images by correlating single-pixel signals with synchronized transformed patterns of Hadamard bases that are actually projected onto the digital micromirror device.Each transform pattern is obtained through the inverse Fourier transform of the pattern acquired by Gaussian filtering of each Hadamard basis in the frequency domain.The proposed scheme is based on a typical SPI experimental setup and does not add any hardware complexity,enabling the transformation of Hadamard matrices and image reconstruction through data processing alone.Therefore,this approach could be considered as an alternative option for achieving fast SPI in a diffraction-limited imaging system,without the need for additional hardware.展开更多
The single-pixel imaging(SPI) technique is able to capture two-dimensional(2 D) images without conventional array sensors by using a photodiode. As a novel scheme, Fourier single-pixel imaging(FSI) has been proven cap...The single-pixel imaging(SPI) technique is able to capture two-dimensional(2 D) images without conventional array sensors by using a photodiode. As a novel scheme, Fourier single-pixel imaging(FSI) has been proven capable of reconstructing high-quality images. Due to the fact that the Fourier basis patterns(also known as grayscale sinusoidal patterns)cannot be well displayed on the digital micromirror device(DMD), a fast FSI system is proposed to solve this problem by binarizing Fourier pattern through a dithering algorithm. However, the traditional dithering algorithm leads to low quality as the extra noise is inevitably induced in the reconstructed images. In this paper, we report a better dithering algorithm to binarize Fourier pattern, which utilizes the Sierra–Lite kernel function by a serpentine scanning method. Numerical simulation and experiment demonstrate that the proposed algorithm is able to achieve higher quality under different sampling ratios.展开更多
A two-stage training method is proposed to enhance imaging quality and reduce reconstruction time in datadriven single-pixel imaging(SPI)under undersampling conditions.This approach leverages a deep learning algorithm...A two-stage training method is proposed to enhance imaging quality and reduce reconstruction time in datadriven single-pixel imaging(SPI)under undersampling conditions.This approach leverages a deep learning algorithm to simulate single-pixel detection and image reconstruction.During the initial training stage,an L2 regularization constraint is imposed on convolution modulation patterns to determine the optimal initial network weights.In the subsequent stage,a coupled deep learning method integrating coded-aperture design and SPI is adopted,which utilizes backpropagation of the loss function to iteratively optimize both the binarized modulation patterns and imaging network parameters.By reducing the binarization errors introduced by the dithering algorithm,this approach improves the quality of data-driven SPI.Compared with traditional deep-learning SPI methods,the proposed method significantly reduces computational complexity,resulting in accelerated image reconstruction.Experimental and simulation results demonstrate the advantages of the method,including high imaging quality,short image reconstruction time,and simplified training.For an image size of 64×64 pixels and 10%sampling rate,the proposed method achieves a peak signal-to-noise ratio of 23.22 dB,structural similarity index of 0.76,and image reconstruction time of approximately 2.57×10^(−4) seconds.展开更多
Photon-level single-pixel imaging overcomes the reliance of traditional imaging techniques on large-scale array detectors,offering the advantages such as high sensitivity,high resolution,and efficient photon utilizati...Photon-level single-pixel imaging overcomes the reliance of traditional imaging techniques on large-scale array detectors,offering the advantages such as high sensitivity,high resolution,and efficient photon utilization.In this paper,we propose a photon-level dynamic feature single-pixel imaging method,leveraging the frequency domain sparsity of the object's dynamic features to construct a compressed measurement system through discrete random photon detection.In the experiments,we successfully captured 167 and 200 Hz featured frequencies and achieved high-quality image reconstruction with a data compression ratio of 20%.Our approach introduces a new detection dimension,significantly expanding the applications of photon-level single-pixel imaging in practical scenarios.展开更多
Existing two-step Fourier single-pixel imaging (FSPI) suffers from low noise-robustness, and three-step FSPI and four-step FSPI improve the noise-robustness but at the cost of more measurements. In this Letter, we pro...Existing two-step Fourier single-pixel imaging (FSPI) suffers from low noise-robustness, and three-step FSPI and four-step FSPI improve the noise-robustness but at the cost of more measurements. In this Letter, we propose a method to improve the noise-robustness of two-step FSPI without additional illumination patterns or measurements. In the proposed method, the measurements from base patterns are replaced by the average values of the measurement from two sets of phase-shift patterns. Thus, the imaging degradation caused by the noise in the measurements from base patterns can be avoided, and more reliable Fourier spectral coefficients are obtained. The imaging quality of the proposed robust two-step FSPI is similar to those of three-step FSPI and four-step FSPI. Simulations and experimental results validate the effectiveness of the proposed method.展开更多
Neutron-induced gamma-ray imaging is a spectroscopic technique that uses characteristic gamma rays to infer the elemental distribution of an object.Currently,this technique requires the use of large facilities to supp...Neutron-induced gamma-ray imaging is a spectroscopic technique that uses characteristic gamma rays to infer the elemental distribution of an object.Currently,this technique requires the use of large facilities to supply a high neutron flux and a time-consuming detection procedure involving direct collimating measurements.In this study,a new method based on low neutron flux was proposed.A single-pixel gamma-ray detector combined with random pattern gamma-ray masks was used to measure the characteristic gamma rays emitted from the sample.Images of the elemental distribution in the sample,comprising 30×30 pixels,were reconstructed using the maximum-likelihood expectation-maximization algorithm.The results demonstrate that the elemental imaging of the sample can be accurately determined using this method.The proposed approach,which eliminates the need for high neutron flux and scanning measurements,can be used for in-field imaging applications.展开更多
Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly diffic...Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly difficult mines and meet the requirements of environmental protection and safety regulations.It promotes the development of a circular economy in mines through the development of lowgrade resources and the resource utilization of waste,and extends the service life of mines.The mass concentration of solid content(abbreviated as“concentration”)is a critical parameter for CPB.However,discrepancies often arise between the on-site measurements and the pre-designed values due to factors such as groundwater inflow and segregation within the goaf,which cannot be evaluated after the solidification of CPB.This paper innovatively provides an in-situ non-destructive approach to identify the real concentration of CPB after curing for certain days using hyperspectral imaging(HSI)technology.Initially,the spectral variation patterns under different concentration conditions were investigated through hyperspectral scanning experiments on CPB samples.The results demonstrate that as the CPB concentration increases from 61wt%to 73wt%,the overall spectral reflectance gradually increases,with two distinct absorption peaks observed at 1407 and 1917 nm.Notably,the reflectance at 1407 nm exhibited a strong linear relationship with the concentration.Subsequently,the K-nearest neighbors(KNN)and support vector machine(SVM)algorithms were employed to classify and identify different concentrations.The study revealed that,with the KNN algorithm,the highest accuracy was achieved when K(number of nearest neighbors)was 1,although this resulted in overfitting.When K=3,the model displayed the optimal balance between accuracy and stability,with an accuracy of 95.03%.In the SVM algorithm,the highest accuracy of 98.24%was attained with parameters C(regularization parameter)=200 and Gamma(kernel coefficient)=10.A comparative analysis of precision,accuracy,and recall further highlighted that the SVM provided superior stability and precision for identifying CPB concentration.Thus,HSI technology offers an effective solution for the in-situ,non-destructive monitoring of CPB concentration,presenting a promising approach for optimizing and controlling CPB characteristic parameters.展开更多
We present a prototype for hybrid Compton and positron emission tomography(PET)imaging aimed at enhancing data utilization and enabling concurrent imaging of multiple radiopharmaceuticals.The prototype comprises two d...We present a prototype for hybrid Compton and positron emission tomography(PET)imaging aimed at enhancing data utilization and enabling concurrent imaging of multiple radiopharmaceuticals.The prototype comprises two detectors that utilize LYSO-SiPM and were available in our laboratory.One detector consists of a 50×50 array of LYSO crystals,each measuring 0.9mm×0.9mm×10mm with 1 mm pitches,whereas the other detector comprises a 25×25 array of LYSO crystals,each measuring 1.9mm×1.9mm×10mm with 2 mm pitches.These detectors are mounted on a rotational stage,which enables them to function as either a Compton camera or a PET detector pair.The 64-channel signals from the SiPMs of each detector are processed through a capacitive multiplexing circuit to yield four position-weighted outputs.Distinct energy windows were used to discriminate Compton events from PET events.Energy resolution and energy-channel relationships were calibrated via multiple sources.The measured average energy resolutions(full widths at half maximum,FWHMs)for the detectors at 511 keV were 17.5%and 15.2%,respectively.The initial experimental results indicate an angular resolution(FWHM)of 8.6◦for the system in Compton imaging mode.A V-shaped tube injected with 18 F solution was clearly reconstructed,which further verified the imaging capabilities of the system in Compton imaging mode.The results of simulation and experimental imaging studies show that the system can detect tumors as small as 1 mm in diameter when working in PET imaging mode.Mouse bone PET imaging was successfully conducted,with the results matching well with the corresponding CT images.This technology holds great potential for advancing the development of physiological function modalities.展开更多
Aqueous zinc metal batteries(AZMBs)face significant challenges in achieving reversibility and cycling stability,primarily due to hydrogen evolution reactions(HER)and zinc dendrite growth.In this study,by employing car...Aqueous zinc metal batteries(AZMBs)face significant challenges in achieving reversibility and cycling stability,primarily due to hydrogen evolution reactions(HER)and zinc dendrite growth.In this study,by employing carefully designed cells that approximate the structural characteristics of practical batteries,we revisit this widely held view through in-operando X-ray radiography to examine zinc dendrite formation and HER under nearpractical operating conditions.While conventional understanding emphasizes the severity of these processes,our findings suggest that zinc dendrites and HER are noticeably less pronounced in dense,real-operation configurations compared to modified cells,possibly due to a more uniform electric field and the suppression of triple-phase boundaries.This study indicates that other components,such as degradation at the cathode current collector interface and configuration mismatches within the full cell,may also represent important barriers to the practical application of AZMBs,particularly during the early stages of electrodeposition.展开更多
Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience...Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience-dependent mechanisms.The pruning process involves multiple molecular signals and a series of regulatory activities governing the“eat me”and“don't eat me”states.Under physiological conditions,the interaction between glial cells and neurons results in the clearance of unnecessary synapses,maintaining normal neural circuit functionality via synaptic pruning.Alterations in genetic and environmental factors can lead to imbalanced synaptic pruning,thus promoting the occurrence and development of autism spectrum disorder,schizophrenia,Alzheimer's disease,and other neurological disorders.In this review,we investigated the molecular mechanisms responsible for synaptic pruning during neural development.We focus on how synaptic pruning can regulate neural circuits and its association with neurological disorders.Furthermore,we discuss the application of emerging optical and imaging technologies to observe synaptic structure and function,as well as their potential for clinical translation.Our aim was to enhance our understanding of synaptic pruning during neural development,including the molecular basis underlying the regulation of synaptic function and the dynamic changes in synaptic density,and to investigate the potential role of these mechanisms in the pathophysiology of neurological diseases,thus providing a theoretical foundation for the treatment of neurological disorders.展开更多
Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in hu...Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in humans remain unclear,including cell viability,distribution,migration,and fate.Conventional cell tracing methods cannot be used in the clinic.The use of superparamagnetic iron oxide nanoparticles as contrast agents allows for the observation of transplanted cells using magnetic resonance imaging.In 2016,the National Medical Products Administration of China approved a new superparamagnetic iron oxide nanoparticle,Ruicun,for use as a contrast agent in clinical trials.In the present study,an acute hemi-transection spinal cord injury model was established in beagle dogs.The injury was then treated by transplantation of Ruicun-labeled mesenchymal stromal cells.The results indicated that Ruicunlabeled mesenchymal stromal cells repaired damaged spinal cord fibers and partially restored neurological function in animals with acute spinal cord injury.T2*-weighted imaging revealed low signal areas on both sides of the injured spinal cord.The results of quantitative susceptibility mapping with ultrashort echo time sequences indicated that Ruicun-labeled mesenchymal stromal cells persisted stably within the injured spinal cord for over 4 weeks.These findings suggest that magnetic resonance imaging has the potential to effectively track the migration of Ruicun-labeled mesenchymal stromal cells and assess their ability to repair spinal cord injury.展开更多
Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indice...Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.展开更多
Typical single-pixel imaging techniques for edge detection are mostly based on first-order differential edge detection operators.In this paper,we present a novel edge detection scheme combining Fourier single-pixel im...Typical single-pixel imaging techniques for edge detection are mostly based on first-order differential edge detection operators.In this paper,we present a novel edge detection scheme combining Fourier single-pixel imaging with a second-order Laplacian of Gaussian(LoG)operator.This method utilizes the convolution results of an LoG operator and Fourier basis patterns as the modulated patterns to extract the edge detail of an unknown object without imaging it.The simulation and experimental results demonstrate that our scheme can ensure finer edge detail,especially under a noisy environment,and save half the processing time when compared with a traditional first-order Sobel operator.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62001249 and62375140)。
文摘Color Fourier single-pixel imaging(FSI)enables efficient spectral and spatial imaging.Here,we propose a Fourier single-pixel imaging scheme with a random color filter array(FSI-RCFA).The proposed method employs a random color filter array(RCFA)to modulate Fourier patterns.A three-step phase-shifting technique reconstructs the Fourier spectrum,followed by an RCFA-based demosaicing algorithm to recover color images.Compared to traditional color FSI based on Bayer color filter array schemes(FSI-BCFA),our approach achieves superior separation between chrominance and luminance components in the frequency domain.Simulation results demonstrate that the FSI-RCFA method achieves a lower mean squared error(MSE),a higher peak signal-to-noise ratio(PSNR),and superior noise resistance compared to FSI-BCFA,while enabling direct single-channel pixel measurements for targeted applications such as agricultural defect detection.
基金upported by the National Natural Science Foundation of China(Grant No.62305184)the Major Key Project of Pengcheng Laboratory(Grant No.PCL2024A1)+1 种基金the Basic and Applied Basic Research Foundation of Guangdong Province(Grant No.2023A1515012932)the Science,Technology and Innovation Commission of Shenzhen Municipality(Grant No.WDZC20220818100259004).
文摘Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ultra-low sampling rates.We develop an alternative optimization with physics and a data-driven diffusion network(APD-Net).It features alternative optimization driven by the learned task-agnostic natural image prior and the task-specific physics prior.During the training stage,APD-Net harnesses the power of diffusion models to capture data-driven statistics of natural signals.In the inference stage,the physics prior is introduced as corrective guidance to ensure consistency between the physics imaging model and the natural image probability distribution.Through alternative optimization,APD-Net reconstructs data-efficient,high-fidelity images that are statistically and physically compliant.To accelerate reconstruction,initializing images with the inverse SPI physical model reduces the need for reconstruction inference from 100 to 30 steps.Through both numerical simulations and real prototype experiments,APD-Net achieves high-quality,full-color reconstructions of complex natural images at a low sampling rate of 1%.In addition,APD-Net’s tuning-free nature ensures robustness across various imaging setups and sampling rates.Our research offers a broadly applicable approach for various applications,including but not limited to medical imaging and industrial inspection.
基金supported by the National Natural Science Foundation of China(Grant Nos.62322502,62131003,and 62088101)the Guangdong Province Key Laboratory of Intelligent Detection in Complex Environment of Aerospace,Land and Sea(Grant No.2022KSYS016).
文摘Existing single-pixel imaging(SPI)and sensing techniques suffer from poor reconstruction quality and heavy computation cost,limiting their widespread application.To tackle these challenges,we propose a large-scale single-pixel imaging and sensing(SPIS)technique that enables high-quality megapixel SPI and highly efficient image-free sensing with a low sampling rate.Specifically,we first scan and sample the entire scene using small-size optimized patterns to obtain information-coupled measurements.Compared with the conventional full-sized patterns,small-sized optimized patterns achieve higher imaging fidelity and sensing accuracy with 1 order of magnitude fewer pattern parameters.Next,the coupled measurements are processed through a transformer-based encoder to extract high-dimensional features,followed by a task-specific plugand-play decoder for imaging or image-free sensing.Considering that the regions with rich textures and edges are more difficult to reconstruct,we use an uncertainty-driven self-adaptive loss function to reinforce the network’s attention to these regions,thereby improving the imaging and sensing performance.Extensive experiments demonstrate that the reported technique achieves 24.13 dB megapixel SPI at a sampling rate of 3%within 1 s.In terms of sensing,it outperforms existing methods by 12%on image-free segmentation accuracy and achieves state-of-the-art image-free object detection accuracy with an order of magnitude less data bandwidth.
基金supported by the Natural Science Foundation of China Project(No.62525102).
文摘Single-pixel imaging(SPI)is a prominent scattering media imaging technique that allows image transmission via one-dimensional detection under structured illumination,with applications spanning from long-range imaging to microscopy.Recent advancements leveraging deep learning(DL)have significantly improved SPI performance,especially at low compression ratios.However,most DL-based SPI methods proposed so far rely heavily on extensive labeled datasets for supervised training,which are often impractical in real-world scenarios.Here,we propose an unsupervised learningenabled label-free SPI method for resilient information transmission through unknown dynamic scattering media.Additionally,we introduce a physics-informed autoencoder framework to optimize encoding schemes,further enhancing image quality at low compression ratios.Simulation and experimental results demonstrate that high-efficiency data transmission with structural similarity exceeding 0.9 is achieved through challenging turbulent channels.Moreover,experiments demonstrate that in a 5 m underwater dynamic turbulent channel,USAF target imaging quality surpasses traditional methods by over 13 dB.The compressive encoded transmission of 720×720 resolution video exceeding 30 seconds with great fidelity is also successfully demonstrated.These preliminary results suggest that our proposed method opens up a new paradigm for resilient information transmission through unknown dynamic scattering media and holds potential for broader applications within many other scattering media imaging technologies.
文摘In recent years,deep learning has been introduced into the field of Single-pixel imaging(SPI),garnering significant attention.However,conventional networks still exhibit limitations in preserving image details.To address this issue,we integrate Large Kernel Convolution(LKconv)into the U-Net framework,proposing an enhanced network structure named U-LKconv network,which significantly enhances the capability to recover image details even under low sampling conditions.
基金Project supported by the Natural Science Foundation of Hebei Province,China(Grant Nos.A2022201039 and F2019201446)the MultiYear Research Grant of University of Macao,China(Grant No.MYRG2020-00082-IAPME)+2 种基金the Science and Technology Development Fund from Macao SAR(FDCT),China(Grant No.0062/2020/AMJ)the Advanced Talents Incubation Program of the Hebei University(Grant No.8012605)the National Natural Science Foundation of China(Grant Nos.11204062,61774053,and 11674273)。
文摘We propose a method of complex-amplitude Fourier single-pixel imaging(CFSI)with coherent structured illumination to acquire both the amplitude and phase of an object.In the proposed method,an object is illustrated by a series of coherent structured light fields,which are generated by a phase-only spatial light modulator,the complex Fourier spectrum of the object can be acquired sequentially by a single-pixel photodetector.Then the desired complex-amplitude image can be retrieved directly by applying an inverse Fourier transform.We experimentally implemented this CFSI with several different types of objects.The experimental results show that the proposed method provides a promising complex-amplitude imaging approach with high quality and a stable configuration.Thus,it might find broad applications in optical metrology and biomedical science.
基金Project supported by the National Natural Science Foundation of China(Grant No.62075241).
文摘Single-pixel imaging(SPI)can transform 2D or 3D image data into 1D light signals,which offers promising prospects for image compression and transmission.However,during data communication these light signals in public channels will easily draw the attention of eavesdroppers.Here,we introduce an efficient encryption method for SPI data transmission that uses the 3D Arnold transformation to directly disrupt 1D single-pixel light signals and utilizes the elliptic curve encryption algorithm for key transmission.This encryption scheme immediately employs Hadamard patterns to illuminate the scene and then utilizes the 3D Arnold transformation to permutate the 1D light signal of single-pixel detection.Then the transformation parameters serve as the secret key,while the security of key exchange is guaranteed by an elliptic curve-based key exchange mechanism.Compared with existing encryption schemes,both computer simulations and optical experiments have been conducted to demonstrate that the proposed technique not only enhances the security of encryption but also eliminates the need for complicated pattern scrambling rules.Additionally,this approach solves the problem of secure key transmission,thus ensuring the security of information and the quality of the decrypted images.
基金Project supported by the National Key Research and Development Program of China (Grant No.2018YFB0504302)。
文摘We propose a single-pixel imaging(SPI)method to achieve a higher-resolution image via the Hadamard transform matrix.Unlike traditional SPI schemes,this new method recovers images by correlating single-pixel signals with synchronized transformed patterns of Hadamard bases that are actually projected onto the digital micromirror device.Each transform pattern is obtained through the inverse Fourier transform of the pattern acquired by Gaussian filtering of each Hadamard basis in the frequency domain.The proposed scheme is based on a typical SPI experimental setup and does not add any hardware complexity,enabling the transformation of Hadamard matrices and image reconstruction through data processing alone.Therefore,this approach could be considered as an alternative option for achieving fast SPI in a diffraction-limited imaging system,without the need for additional hardware.
基金supported by the National Natural Science Foundation of China(Grant No.61271376)the Anhui Provincial Natural Science Foundation,China(Grant No.1208085MF114)
文摘The single-pixel imaging(SPI) technique is able to capture two-dimensional(2 D) images without conventional array sensors by using a photodiode. As a novel scheme, Fourier single-pixel imaging(FSI) has been proven capable of reconstructing high-quality images. Due to the fact that the Fourier basis patterns(also known as grayscale sinusoidal patterns)cannot be well displayed on the digital micromirror device(DMD), a fast FSI system is proposed to solve this problem by binarizing Fourier pattern through a dithering algorithm. However, the traditional dithering algorithm leads to low quality as the extra noise is inevitably induced in the reconstructed images. In this paper, we report a better dithering algorithm to binarize Fourier pattern, which utilizes the Sierra–Lite kernel function by a serpentine scanning method. Numerical simulation and experiment demonstrate that the proposed algorithm is able to achieve higher quality under different sampling ratios.
文摘A two-stage training method is proposed to enhance imaging quality and reduce reconstruction time in datadriven single-pixel imaging(SPI)under undersampling conditions.This approach leverages a deep learning algorithm to simulate single-pixel detection and image reconstruction.During the initial training stage,an L2 regularization constraint is imposed on convolution modulation patterns to determine the optimal initial network weights.In the subsequent stage,a coupled deep learning method integrating coded-aperture design and SPI is adopted,which utilizes backpropagation of the loss function to iteratively optimize both the binarized modulation patterns and imaging network parameters.By reducing the binarization errors introduced by the dithering algorithm,this approach improves the quality of data-driven SPI.Compared with traditional deep-learning SPI methods,the proposed method significantly reduces computational complexity,resulting in accelerated image reconstruction.Experimental and simulation results demonstrate the advantages of the method,including high imaging quality,short image reconstruction time,and simplified training.For an image size of 64×64 pixels and 10%sampling rate,the proposed method achieves a peak signal-to-noise ratio of 23.22 dB,structural similarity index of 0.76,and image reconstruction time of approximately 2.57×10^(−4) seconds.
基金supported by the Shanxi Province Science and Technology Major Special Project(No.202201010101005)the Innovation Program for Quantum Science and Technology(No.2021ZD0300705)+6 种基金the National Natural Science Foundation of China(Nos.U23A20380,62105193,62127817,62075120,62075122,U22A2091,62222509,62205187,and62305200)the China Postdoctoral Science Foundation(No.2022M722006)the National Key Research and Development Program of China(No.2022YFA1404201)the Shanxi Province Science and Technology Innovation Talent Team(No.202204051001014)the Science and Technology Cooperation Project of Shanxi Province(No.202104041101021)the Shanxi“1331 Project”111 Projects(No.D18001)。
文摘Photon-level single-pixel imaging overcomes the reliance of traditional imaging techniques on large-scale array detectors,offering the advantages such as high sensitivity,high resolution,and efficient photon utilization.In this paper,we propose a photon-level dynamic feature single-pixel imaging method,leveraging the frequency domain sparsity of the object's dynamic features to construct a compressed measurement system through discrete random photon detection.In the experiments,we successfully captured 167 and 200 Hz featured frequencies and achieved high-quality image reconstruction with a data compression ratio of 20%.Our approach introduces a new detection dimension,significantly expanding the applications of photon-level single-pixel imaging in practical scenarios.
基金supported by the Innovation Project of Optics Valley Laboratory(No.OVL2023ZD005).
文摘Existing two-step Fourier single-pixel imaging (FSPI) suffers from low noise-robustness, and three-step FSPI and four-step FSPI improve the noise-robustness but at the cost of more measurements. In this Letter, we propose a method to improve the noise-robustness of two-step FSPI without additional illumination patterns or measurements. In the proposed method, the measurements from base patterns are replaced by the average values of the measurement from two sets of phase-shift patterns. Thus, the imaging degradation caused by the noise in the measurements from base patterns can be avoided, and more reliable Fourier spectral coefficients are obtained. The imaging quality of the proposed robust two-step FSPI is similar to those of three-step FSPI and four-step FSPI. Simulations and experimental results validate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(Nos.12105143 and 11975121)the China Postdoctoral Science Foundation(No.2023M741453)+1 种基金the Engineering Research Center of Nuclear Technology Application(No.HJSJYB2020-1)the Key Laboratory of Ionizing Radiation Metering and Safety Evaluation for Jiangsu Province Market Regulation,and the Jiangsu Province Excellent Postdoctoral Program(No.JB23057).
文摘Neutron-induced gamma-ray imaging is a spectroscopic technique that uses characteristic gamma rays to infer the elemental distribution of an object.Currently,this technique requires the use of large facilities to supply a high neutron flux and a time-consuming detection procedure involving direct collimating measurements.In this study,a new method based on low neutron flux was proposed.A single-pixel gamma-ray detector combined with random pattern gamma-ray masks was used to measure the characteristic gamma rays emitted from the sample.Images of the elemental distribution in the sample,comprising 30×30 pixels,were reconstructed using the maximum-likelihood expectation-maximization algorithm.The results demonstrate that the elemental imaging of the sample can be accurately determined using this method.The proposed approach,which eliminates the need for high neutron flux and scanning measurements,can be used for in-field imaging applications.
基金funded by the National Natural Science Foundation of China(Nos.52474165 and 52522404)。
文摘Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly difficult mines and meet the requirements of environmental protection and safety regulations.It promotes the development of a circular economy in mines through the development of lowgrade resources and the resource utilization of waste,and extends the service life of mines.The mass concentration of solid content(abbreviated as“concentration”)is a critical parameter for CPB.However,discrepancies often arise between the on-site measurements and the pre-designed values due to factors such as groundwater inflow and segregation within the goaf,which cannot be evaluated after the solidification of CPB.This paper innovatively provides an in-situ non-destructive approach to identify the real concentration of CPB after curing for certain days using hyperspectral imaging(HSI)technology.Initially,the spectral variation patterns under different concentration conditions were investigated through hyperspectral scanning experiments on CPB samples.The results demonstrate that as the CPB concentration increases from 61wt%to 73wt%,the overall spectral reflectance gradually increases,with two distinct absorption peaks observed at 1407 and 1917 nm.Notably,the reflectance at 1407 nm exhibited a strong linear relationship with the concentration.Subsequently,the K-nearest neighbors(KNN)and support vector machine(SVM)algorithms were employed to classify and identify different concentrations.The study revealed that,with the KNN algorithm,the highest accuracy was achieved when K(number of nearest neighbors)was 1,although this resulted in overfitting.When K=3,the model displayed the optimal balance between accuracy and stability,with an accuracy of 95.03%.In the SVM algorithm,the highest accuracy of 98.24%was attained with parameters C(regularization parameter)=200 and Gamma(kernel coefficient)=10.A comparative analysis of precision,accuracy,and recall further highlighted that the SVM provided superior stability and precision for identifying CPB concentration.Thus,HSI technology offers an effective solution for the in-situ,non-destructive monitoring of CPB concentration,presenting a promising approach for optimizing and controlling CPB characteristic parameters.
基金supported by the National Natural Science Foundation of China(No.12105018)the Beijing Nova Program(Nos.Z211100002121129 and 20230484413)the Beijing Normal University Start-up Grant(No.312232104).
文摘We present a prototype for hybrid Compton and positron emission tomography(PET)imaging aimed at enhancing data utilization and enabling concurrent imaging of multiple radiopharmaceuticals.The prototype comprises two detectors that utilize LYSO-SiPM and were available in our laboratory.One detector consists of a 50×50 array of LYSO crystals,each measuring 0.9mm×0.9mm×10mm with 1 mm pitches,whereas the other detector comprises a 25×25 array of LYSO crystals,each measuring 1.9mm×1.9mm×10mm with 2 mm pitches.These detectors are mounted on a rotational stage,which enables them to function as either a Compton camera or a PET detector pair.The 64-channel signals from the SiPMs of each detector are processed through a capacitive multiplexing circuit to yield four position-weighted outputs.Distinct energy windows were used to discriminate Compton events from PET events.Energy resolution and energy-channel relationships were calibrated via multiple sources.The measured average energy resolutions(full widths at half maximum,FWHMs)for the detectors at 511 keV were 17.5%and 15.2%,respectively.The initial experimental results indicate an angular resolution(FWHM)of 8.6◦for the system in Compton imaging mode.A V-shaped tube injected with 18 F solution was clearly reconstructed,which further verified the imaging capabilities of the system in Compton imaging mode.The results of simulation and experimental imaging studies show that the system can detect tumors as small as 1 mm in diameter when working in PET imaging mode.Mouse bone PET imaging was successfully conducted,with the results matching well with the corresponding CT images.This technology holds great potential for advancing the development of physiological function modalities.
基金the fundamental Research Funds for the central Universities(x2wjD2240360)for the funding supportMeanwhile,Engineering and Physical Sciences Research Council(EPSRC,EP/V027433/3)+2 种基金UK Research and Innovation(UKRI)under the UK government’s Horizon Europe funding(101077226,EP/Y008707/1)Faraday Institution(EP/S003053/1)Degradation project(FIRG001),Royal Society(IEC\NSFC\233361),QUB Agility Fund and Wright Technology and Research Centre(W-Tech,R5240MEE)Funding from UK aid from the UK Government through the Faraday Institution and the Transforming Energy Access Programme(Grant number FIRG050-Device engineering of Zn-based hybrid micro-flow batteries and by-product H2 collection for Emerging Economies)。
文摘Aqueous zinc metal batteries(AZMBs)face significant challenges in achieving reversibility and cycling stability,primarily due to hydrogen evolution reactions(HER)and zinc dendrite growth.In this study,by employing carefully designed cells that approximate the structural characteristics of practical batteries,we revisit this widely held view through in-operando X-ray radiography to examine zinc dendrite formation and HER under nearpractical operating conditions.While conventional understanding emphasizes the severity of these processes,our findings suggest that zinc dendrites and HER are noticeably less pronounced in dense,real-operation configurations compared to modified cells,possibly due to a more uniform electric field and the suppression of triple-phase boundaries.This study indicates that other components,such as degradation at the cathode current collector interface and configuration mismatches within the full cell,may also represent important barriers to the practical application of AZMBs,particularly during the early stages of electrodeposition.
基金supported by the National Natural Science Foundation of China,No.31760290,82160688the Key Development Areas Project of Ganzhou Science and Technology,No.2022B-SF9554(all to XL)。
文摘Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience-dependent mechanisms.The pruning process involves multiple molecular signals and a series of regulatory activities governing the“eat me”and“don't eat me”states.Under physiological conditions,the interaction between glial cells and neurons results in the clearance of unnecessary synapses,maintaining normal neural circuit functionality via synaptic pruning.Alterations in genetic and environmental factors can lead to imbalanced synaptic pruning,thus promoting the occurrence and development of autism spectrum disorder,schizophrenia,Alzheimer's disease,and other neurological disorders.In this review,we investigated the molecular mechanisms responsible for synaptic pruning during neural development.We focus on how synaptic pruning can regulate neural circuits and its association with neurological disorders.Furthermore,we discuss the application of emerging optical and imaging technologies to observe synaptic structure and function,as well as their potential for clinical translation.Our aim was to enhance our understanding of synaptic pruning during neural development,including the molecular basis underlying the regulation of synaptic function and the dynamic changes in synaptic density,and to investigate the potential role of these mechanisms in the pathophysiology of neurological diseases,thus providing a theoretical foundation for the treatment of neurological disorders.
基金supported by the National Key R&D Program of China,Nos.2017YFA0104302(to NG and XM)and 2017YFA0104304(to BW and ZZ)
文摘Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in humans remain unclear,including cell viability,distribution,migration,and fate.Conventional cell tracing methods cannot be used in the clinic.The use of superparamagnetic iron oxide nanoparticles as contrast agents allows for the observation of transplanted cells using magnetic resonance imaging.In 2016,the National Medical Products Administration of China approved a new superparamagnetic iron oxide nanoparticle,Ruicun,for use as a contrast agent in clinical trials.In the present study,an acute hemi-transection spinal cord injury model was established in beagle dogs.The injury was then treated by transplantation of Ruicun-labeled mesenchymal stromal cells.The results indicated that Ruicunlabeled mesenchymal stromal cells repaired damaged spinal cord fibers and partially restored neurological function in animals with acute spinal cord injury.T2*-weighted imaging revealed low signal areas on both sides of the injured spinal cord.The results of quantitative susceptibility mapping with ultrashort echo time sequences indicated that Ruicun-labeled mesenchymal stromal cells persisted stably within the injured spinal cord for over 4 weeks.These findings suggest that magnetic resonance imaging has the potential to effectively track the migration of Ruicun-labeled mesenchymal stromal cells and assess their ability to repair spinal cord injury.
基金supported by the National Natural Science Foundation of China,No.82071909(to GF)the Natural Science Foundation of Liaoning Province,No.2023-MS-07(to HL)。
文摘Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.
基金supported by the National Natural Science Foundation of China(Nos.61871431,61971184,and 62001162)China Postdoctoral Science Foundation(No.2019M662767)。
文摘Typical single-pixel imaging techniques for edge detection are mostly based on first-order differential edge detection operators.In this paper,we present a novel edge detection scheme combining Fourier single-pixel imaging with a second-order Laplacian of Gaussian(LoG)operator.This method utilizes the convolution results of an LoG operator and Fourier basis patterns as the modulated patterns to extract the edge detail of an unknown object without imaging it.The simulation and experimental results demonstrate that our scheme can ensure finer edge detail,especially under a noisy environment,and save half the processing time when compared with a traditional first-order Sobel operator.
