Obtaining clear images of underwater scenes with descriptive details is an arduous task.Conventional imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition er...Obtaining clear images of underwater scenes with descriptive details is an arduous task.Conventional imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition errors.Consequently,a need for a system that produces clear images for underwater image study has been necessitated.To overcome problems in resolution and to make better use of the Super-Resolution(SR)method,this paper introduces a novel method that has been derived from the Alpha Generative Adversarial Network(AlphaGAN)model,named Alpha Super Resolution Generative Adversarial Network(AlphaSRGAN).The model put forth in this paper helps in enhancing the quality of underwater imagery and yields images with greater resolution and more concise details.Images undergo pre-processing before they are fed into a generator network that optimizes and reforms the structure of the network while enhancing the stability of the network that acts as the generator.After the images are processed by the generator network,they are passed through an adversarial method for training models.The dataset used in this paper to learn Single Image Super Resolution(SISR)is the USR 248 dataset.Training supervision is performed by an unprejudiced function that simultaneously scrutinizes and improves the image quality.Appraisal of images is done with reference to factors like local style information,global content and color.The dataset USR 248 which has a huge collection of images has been used for the study is composed of three collections of images—high(640×480)and low(80×60,160×120,and 320×240).Paired instances of different sizes—2×,4×and 8×—are also present in the dataset.Parameters like Mean Opinion Score(MOS),Peak Signal-to-Noise Ratio(PSNR),Structural Similarity(SSIM)and Underwater Image Quality Measure(UIQM)scores have been compared to validate the improved efficiency of our model when compared to existing works.展开更多
Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely exp...Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.展开更多
Spontaneously blinking probe, which switches between dark and bright state without UV or external additives, is extremely attractive in super resolution imaging of live cells. Herein, a clickable rhodamine spirolactam...Spontaneously blinking probe, which switches between dark and bright state without UV or external additives, is extremely attractive in super resolution imaging of live cells. Herein, a clickable rhodamine spirolactam probe, Atto565-Tet, is rationally constructed for spontaneously blinking after biorthogonal labelling and successfully applied to super resolution imaging of mitochondria and lysosomes.展开更多
As an ill-posed problem, multiframe blind super resolution imaging recovers a high resolution image from a group of low resolution images with some degradations when the information of blur kernel is limited. Note tha...As an ill-posed problem, multiframe blind super resolution imaging recovers a high resolution image from a group of low resolution images with some degradations when the information of blur kernel is limited. Note that the quality of the recovered image is influenced more by the accuracy of blur estimation than an advanced regularization. We study the traditional model of the multiframe super resolution and modify it for blind deblurring. Based on the analysis, we proposed two algorithms. The first one is based on the total variation blind deconvolution algorithm and formulated as a functional for optimization with the regularization of blur. Based on the alternating minimization and the gradient descent algorithm, the high resolution image and the unknown blur kernel are estimated iteratively. By using the median shift and add operator, the second algorithm is more robust to the outlier influence. The MSAA initialization simplifies the interpolation process to reconstruct the blurred high resolution image for blind deblurring and improves the accuracy of blind super resolution imaging. The experimental results demonstrate the superiority and accuracy of our novel algorithms.展开更多
We report a comprehensive numerical study of super resolution (SR) structured illumination microscopy (SIM) utilizing the classic Heintzmann-Cremer SIM process and algorithm. In particular, we investigated the impact ...We report a comprehensive numerical study of super resolution (SR) structured illumination microscopy (SIM) utilizing the classic Heintzmann-Cremer SIM process and algorithm. In particular, we investigated the impact of the diffraction limit of the underlying imaging system on the optimal SIM grating frequency that can be used to obtain the highest SR enhancement with non-continuous spatial frequency support. Besides confirming the previous theoretical and experimental work that SR-SIM can achieve an enhancement close to 3 times the diffraction limit with grating pattern illuminations, we also observe and report a series of more subtle effects of SR-SIM with non-continuous spatial frequency support. Our simulations show that when the SIM grating frequency exceeds twice that of the diffraction limit, the higher SIM grating frequency can help achieve a higher SR enhancement for the underlying imaging systems whose diffraction limit is low, though this enhancement is obtained at the cost of losing resolution at some lower resolution targets. Our simulations also show that, for underlying imaging systems with high diffraction limits, however, SR-SIM grating frequencies above twice the diffraction limits tend to bring no significant extra enhancement. Furthermore, we observed that there exists a limit grating frequency above which the SR enhancement effect is lost, and the reconstructed images essentially have the same resolution as the one obtained directly from the underlying imaging system without using the SIM process.展开更多
Super-resolution imaging has revolutionized our ability to visualize biological structures at subcellular scales.However,deep-tissue super-resolution imaging remains constrained by background interference,which leads ...Super-resolution imaging has revolutionized our ability to visualize biological structures at subcellular scales.However,deep-tissue super-resolution imaging remains constrained by background interference,which leads to limited depth penetration and compromised imaging fidelity.To overcome these challenges,we propose a novel imaging system,confocal2 spinning-disk image scanning microscopy(C^(2)SD-ISM).It integrates a spinning-disk(SD)confocal microscope,which physically eliminates out-of-focus signals,forming the first confocal level.A digital micromirror device(DMD)is employed for sparse multifocal illumination,combined with a dynamic pinhole array pixel reassignment(DPA-PR)algorithm for ISM super-resolution reconstruction,forming the second confocal level.The dual confocal configuration enhances system resolution,while effectively mitigating scattering background interference.Compared to computational out-of-focus signal removal,SD preserves the original intensity distribution as the penetration depth increases,achieving an imaging depth of up to 180μm.Additionally,the DPA-PR algorithm effectively corrects Stokes shifts,optical aberrations,and other non-ideal conditions,achieving a lateral resolution of 144 nm and an axial resolution of 351 nm,and a linear correlation of up to 92%between the original confocal and the reconstructed image,thereby enabling high-fidelity super-resolution imaging.Moreover,the system's programmable illumination via the DMD allows for seamless realization with structured illumination microscopy modality,offering excellent scalability and ease of use.Altogether,these capabilities make the C^(2)SD-ISM system a versatile tool,advancing cellular imaging and tissue-scale exploration for modern bioimaging needs.展开更多
Many networks are designed to stack a large number of residual blocks,deepen the network and improve network performance through short residual connec-tion,long residual connection,and dense connection.However,without...Many networks are designed to stack a large number of residual blocks,deepen the network and improve network performance through short residual connec-tion,long residual connection,and dense connection.However,without consider-ing different contributions of different depth features to the network,these de-signs have the problem of evaluating the importance of different depth features.To solve this problem,this paper proposes an adaptive densely residual net-work(ADRNet)for the single image super resolution.ADRN realizes the evalua-tion of distributions of different depth features and learns more representative features.An adaptive densely residual block(ADRB)was designed,combining 3 residual blocks(RB)and dense connection was added.It learned the attention score of each dense connection through adaptive dense connections,and the at-tention score reflected the importance of the features of each RB.To further en-hance the performance of ADRB,a multi-direction attention block(MDAB)was introduced to obtain multidirectional context information.Through comparative experiments,it is proved that theproposed ADRNet is superior to the existing methods.Through ablation experiments,it is proved that evaluating features of different depths helps to improve network performance.展开更多
Fluorescent probes have revolutionized optical imaging and biosensing by enabling real-time visualization, quantification, and tracking of biological processes at molecular and cellular levels. These probes, ranging f...Fluorescent probes have revolutionized optical imaging and biosensing by enabling real-time visualization, quantification, and tracking of biological processes at molecular and cellular levels. These probes, ranging from organic dyes to genetically encoded proteins and nanomaterials, provide unparalleled specificity, sensitivity, and multiplexing capabilities. However, challenges such as brightness, photobleaching, biocompatibility, and emission range continue to drive innovation in probe design and application. This special issue, comprising four review papers and seven original research studies, highlights cutting-edge advancements in fluorescent probe technologies and their transformative roles in super-resolution imaging, in vivo diagnostics, and cancer therapeutics.展开更多
The global shortage of fossil fuels,along with the widespread pollution caused by their use,urgently calls for the development of reliable clean energy resources.Among various fundamental strategies,the production of ...The global shortage of fossil fuels,along with the widespread pollution caused by their use,urgently calls for the development of reliable clean energy resources.Among various fundamental strategies,the production of green hydrogen through photochemical or electrochemical water splitting has been extensively studied.展开更多
Nature 643,669-674(2025)Photon avalanche is a distinctive optical nonlinear phe-nomenon observed in lanthanide-doped nanocrystals,which holds great potential for super-resolution imaging,ultrasensitive optical sensing...Nature 643,669-674(2025)Photon avalanche is a distinctive optical nonlinear phe-nomenon observed in lanthanide-doped nanocrystals,which holds great potential for super-resolution imaging,ultrasensitive optical sensing,and multiphysics field detec-tion.However,further enhancement of nonlinearity in photon avalanche nanomaterials remains challenging.展开更多
Multi-angle illumination is a widely adopted strategy in various super-resolution imaging systems,where improving computational efficiency and signal-to-noise ratio(SNR)remains a critical challenge.In this study,we pr...Multi-angle illumination is a widely adopted strategy in various super-resolution imaging systems,where improving computational efficiency and signal-to-noise ratio(SNR)remains a critical challenge.In this study,we propose the integration of the iterative kernel correction(IKC)algorithm with a multi-angle(MA)illumination scheme to enhance imaging reconstruction efficiency and SNR.The proposed IKC-MA scheme demonstrates the capability to significantly reduce image acquisition time while achieving high-quality reconstruction within 1 s,without relying on extensive experimental datasets.This ensures broad applicability across diverse imaging scenarios.Experimental results indicate substantial improvements in imaging speed and quality compared to conventional methods,with the IKC-MA model achieving a remarkable reduction in data acquisition time.This approach offers a faster and more generalizable solution for super-resolution microscopic imaging,paving the way for advancements in real-time imaging applications.展开更多
文摘Obtaining clear images of underwater scenes with descriptive details is an arduous task.Conventional imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition errors.Consequently,a need for a system that produces clear images for underwater image study has been necessitated.To overcome problems in resolution and to make better use of the Super-Resolution(SR)method,this paper introduces a novel method that has been derived from the Alpha Generative Adversarial Network(AlphaGAN)model,named Alpha Super Resolution Generative Adversarial Network(AlphaSRGAN).The model put forth in this paper helps in enhancing the quality of underwater imagery and yields images with greater resolution and more concise details.Images undergo pre-processing before they are fed into a generator network that optimizes and reforms the structure of the network while enhancing the stability of the network that acts as the generator.After the images are processed by the generator network,they are passed through an adversarial method for training models.The dataset used in this paper to learn Single Image Super Resolution(SISR)is the USR 248 dataset.Training supervision is performed by an unprejudiced function that simultaneously scrutinizes and improves the image quality.Appraisal of images is done with reference to factors like local style information,global content and color.The dataset USR 248 which has a huge collection of images has been used for the study is composed of three collections of images—high(640×480)and low(80×60,160×120,and 320×240).Paired instances of different sizes—2×,4×and 8×—are also present in the dataset.Parameters like Mean Opinion Score(MOS),Peak Signal-to-Noise Ratio(PSNR),Structural Similarity(SSIM)and Underwater Image Quality Measure(UIQM)scores have been compared to validate the improved efficiency of our model when compared to existing works.
基金the TCL Science and Technology Innovation Fundthe Youth Science and Technology Talent Promotion Project of Jiangsu Association for Science and Technology,Grant/Award Number:JSTJ‐2023‐017+4 种基金Shenzhen Municipal Science and Technology Innovation Council,Grant/Award Number:JSGG20220831105002004National Natural Science Foundation of China,Grant/Award Number:62201468Postdoctoral Research Foundation of China,Grant/Award Number:2022M722599the Fundamental Research Funds for the Central Universities,Grant/Award Number:D5000210966the Guangdong Basic and Applied Basic Research Foundation,Grant/Award Number:2021A1515110079。
文摘Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.
基金supported by the National Natural Science Foundation of China (Nos. 21421005, 21576040, 21776037, 22004011)China Postdoctoral Science Foundation (Nos. BX20200073 and 2020M670754)Dalian Science and Technology Innovation Fund (No. 2020JJ25CY014)。
文摘Spontaneously blinking probe, which switches between dark and bright state without UV or external additives, is extremely attractive in super resolution imaging of live cells. Herein, a clickable rhodamine spirolactam probe, Atto565-Tet, is rationally constructed for spontaneously blinking after biorthogonal labelling and successfully applied to super resolution imaging of mitochondria and lysosomes.
基金Supported by the National Natural Science Foundation of China(No.61340034)the Research Program of Application Foundation and Advanced Technology of Tianjin(No.13JCYBJC15600)
文摘As an ill-posed problem, multiframe blind super resolution imaging recovers a high resolution image from a group of low resolution images with some degradations when the information of blur kernel is limited. Note that the quality of the recovered image is influenced more by the accuracy of blur estimation than an advanced regularization. We study the traditional model of the multiframe super resolution and modify it for blind deblurring. Based on the analysis, we proposed two algorithms. The first one is based on the total variation blind deconvolution algorithm and formulated as a functional for optimization with the regularization of blur. Based on the alternating minimization and the gradient descent algorithm, the high resolution image and the unknown blur kernel are estimated iteratively. By using the median shift and add operator, the second algorithm is more robust to the outlier influence. The MSAA initialization simplifies the interpolation process to reconstruct the blurred high resolution image for blind deblurring and improves the accuracy of blind super resolution imaging. The experimental results demonstrate the superiority and accuracy of our novel algorithms.
文摘We report a comprehensive numerical study of super resolution (SR) structured illumination microscopy (SIM) utilizing the classic Heintzmann-Cremer SIM process and algorithm. In particular, we investigated the impact of the diffraction limit of the underlying imaging system on the optimal SIM grating frequency that can be used to obtain the highest SR enhancement with non-continuous spatial frequency support. Besides confirming the previous theoretical and experimental work that SR-SIM can achieve an enhancement close to 3 times the diffraction limit with grating pattern illuminations, we also observe and report a series of more subtle effects of SR-SIM with non-continuous spatial frequency support. Our simulations show that when the SIM grating frequency exceeds twice that of the diffraction limit, the higher SIM grating frequency can help achieve a higher SR enhancement for the underlying imaging systems whose diffraction limit is low, though this enhancement is obtained at the cost of losing resolution at some lower resolution targets. Our simulations also show that, for underlying imaging systems with high diffraction limits, however, SR-SIM grating frequencies above twice the diffraction limits tend to bring no significant extra enhancement. Furthermore, we observed that there exists a limit grating frequency above which the SR enhancement effect is lost, and the reconstructed images essentially have the same resolution as the one obtained directly from the underlying imaging system without using the SIM process.
基金supported by the National Key R&D Program of China(2022YFC3401100)the National Natural Science Foundation of China(62025501,92150301,62335008,62405010,and 62305004)the Postdoctoral Fellowship Program of CPSF(GZB20250669)。
文摘Super-resolution imaging has revolutionized our ability to visualize biological structures at subcellular scales.However,deep-tissue super-resolution imaging remains constrained by background interference,which leads to limited depth penetration and compromised imaging fidelity.To overcome these challenges,we propose a novel imaging system,confocal2 spinning-disk image scanning microscopy(C^(2)SD-ISM).It integrates a spinning-disk(SD)confocal microscope,which physically eliminates out-of-focus signals,forming the first confocal level.A digital micromirror device(DMD)is employed for sparse multifocal illumination,combined with a dynamic pinhole array pixel reassignment(DPA-PR)algorithm for ISM super-resolution reconstruction,forming the second confocal level.The dual confocal configuration enhances system resolution,while effectively mitigating scattering background interference.Compared to computational out-of-focus signal removal,SD preserves the original intensity distribution as the penetration depth increases,achieving an imaging depth of up to 180μm.Additionally,the DPA-PR algorithm effectively corrects Stokes shifts,optical aberrations,and other non-ideal conditions,achieving a lateral resolution of 144 nm and an axial resolution of 351 nm,and a linear correlation of up to 92%between the original confocal and the reconstructed image,thereby enabling high-fidelity super-resolution imaging.Moreover,the system's programmable illumination via the DMD allows for seamless realization with structured illumination microscopy modality,offering excellent scalability and ease of use.Altogether,these capabilities make the C^(2)SD-ISM system a versatile tool,advancing cellular imaging and tissue-scale exploration for modern bioimaging needs.
文摘Many networks are designed to stack a large number of residual blocks,deepen the network and improve network performance through short residual connec-tion,long residual connection,and dense connection.However,without consider-ing different contributions of different depth features to the network,these de-signs have the problem of evaluating the importance of different depth features.To solve this problem,this paper proposes an adaptive densely residual net-work(ADRNet)for the single image super resolution.ADRN realizes the evalua-tion of distributions of different depth features and learns more representative features.An adaptive densely residual block(ADRB)was designed,combining 3 residual blocks(RB)and dense connection was added.It learned the attention score of each dense connection through adaptive dense connections,and the at-tention score reflected the importance of the features of each RB.To further en-hance the performance of ADRB,a multi-direction attention block(MDAB)was introduced to obtain multidirectional context information.Through comparative experiments,it is proved that theproposed ADRNet is superior to the existing methods.Through ablation experiments,it is proved that evaluating features of different depths helps to improve network performance.
文摘Fluorescent probes have revolutionized optical imaging and biosensing by enabling real-time visualization, quantification, and tracking of biological processes at molecular and cellular levels. These probes, ranging from organic dyes to genetically encoded proteins and nanomaterials, provide unparalleled specificity, sensitivity, and multiplexing capabilities. However, challenges such as brightness, photobleaching, biocompatibility, and emission range continue to drive innovation in probe design and application. This special issue, comprising four review papers and seven original research studies, highlights cutting-edge advancements in fluorescent probe technologies and their transformative roles in super-resolution imaging, in vivo diagnostics, and cancer therapeutics.
文摘The global shortage of fossil fuels,along with the widespread pollution caused by their use,urgently calls for the development of reliable clean energy resources.Among various fundamental strategies,the production of green hydrogen through photochemical or electrochemical water splitting has been extensively studied.
文摘Nature 643,669-674(2025)Photon avalanche is a distinctive optical nonlinear phe-nomenon observed in lanthanide-doped nanocrystals,which holds great potential for super-resolution imaging,ultrasensitive optical sensing,and multiphysics field detec-tion.However,further enhancement of nonlinearity in photon avalanche nanomaterials remains challenging.
基金National Key Research and Development Program of China(2022YFF0712500,2022YFC3401100)Guangdong Major Project of Basic and Applied Basic Research(2020B0301030009)+5 种基金National Natural Science Foundation of China(62375144,61875092,12204017,12004012,12004013,12041602,91750203,91850111,92150301)Beijing-Tianjin-Hebei Basic Research Cooperation Special Program(19JCZDJC65300)China Postdoctoral Science Foundation(2020M680220,2020M680230)Clinical Medicine Plus X-Young Scholars Project,Peking UniversityFundamental Research Funds for the Central UniversitiesHigh-performance Computing Platform of Peking University。
文摘Multi-angle illumination is a widely adopted strategy in various super-resolution imaging systems,where improving computational efficiency and signal-to-noise ratio(SNR)remains a critical challenge.In this study,we propose the integration of the iterative kernel correction(IKC)algorithm with a multi-angle(MA)illumination scheme to enhance imaging reconstruction efficiency and SNR.The proposed IKC-MA scheme demonstrates the capability to significantly reduce image acquisition time while achieving high-quality reconstruction within 1 s,without relying on extensive experimental datasets.This ensures broad applicability across diverse imaging scenarios.Experimental results indicate substantial improvements in imaging speed and quality compared to conventional methods,with the IKC-MA model achieving a remarkable reduction in data acquisition time.This approach offers a faster and more generalizable solution for super-resolution microscopic imaging,paving the way for advancements in real-time imaging applications.
