Deep convolutional neural networks(CNNs)have demonstrated remarkable performance in video super-resolution(VSR).However,the ability of most existing methods to recover fine details in complex scenes is often hindered ...Deep convolutional neural networks(CNNs)have demonstrated remarkable performance in video super-resolution(VSR).However,the ability of most existing methods to recover fine details in complex scenes is often hindered by the loss of shallow texture information during feature extraction.To address this limitation,we propose a 3D Convolutional Enhanced Residual Video Super-Resolution Network(3D-ERVSNet).This network employs a forward and backward bidirectional propagation module(FBBPM)that aligns features across frames using explicit optical flow through lightweight SPyNet.By incorporating an enhanced residual structure(ERS)with skip connections,shallow and deep features are effectively integrated,enhancing texture restoration capabilities.Furthermore,3D convolution module(3DCM)is applied after the backward propagation module to implicitly capture spatio-temporal dependencies.The architecture synergizes these components where FBBPM extracts aligned features,ERS fuses hierarchical representations,and 3DCM refines temporal coherence.Finally,a deep feature aggregation module(DFAM)fuses the processed features,and a pixel-upsampling module(PUM)reconstructs the high-resolution(HR)video frames.Comprehensive evaluations on REDS,Vid4,UDM10,and Vim4 benchmarks demonstrate well performance including 30.95 dB PSNR/0.8822 SSIM on REDS and 32.78 dB/0.8987 on Vim4.3D-ERVSNet achieves significant gains over baselines while maintaining high efficiency with only 6.3M parameters and 77ms/frame runtime(i.e.,20×faster than RBPN).The network’s effectiveness stems from its task-specific asymmetric design that balances explicit alignment and implicit fusion.展开更多
Background Recurrent recovery is a common method for video super-resolution(VSR)that models the correlation between frames via hidden states.However,the application of this structure in real-world scenarios can lead t...Background Recurrent recovery is a common method for video super-resolution(VSR)that models the correlation between frames via hidden states.However,the application of this structure in real-world scenarios can lead to unsatisfactory artifacts.We found that in real-world VSR training,the use of unknown and complex degradation can better simulate the degradation process in the real world.Methods Based on this,we propose the RealFuVSR model,which simulates real-world degradation and mitigates artifacts caused by the VSR.Specifically,we propose a multiscale feature extraction module(MSF)module that extracts and fuses features from multiple scales,thereby facilitating the elimination of hidden state artifacts.To improve the accuracy of the hidden state alignment information,RealFuVSR uses an advanced optical flow-guided deformable convolution.Moreover,a cascaded residual upsampling module was used to eliminate noise caused by the upsampling process.Results The experiment demonstrates that RealFuVSR model can not only recover high-quality videos but also outperforms the state-of-the-art RealBasicVSR and RealESRGAN models.展开更多
This letter proposes a novel method of compressed video super-resolution reconstruction based on MAP-POCS (Maximum Posterior Probability-Projection Onto Convex Set). At first assuming the high-resolution model subject...This letter proposes a novel method of compressed video super-resolution reconstruction based on MAP-POCS (Maximum Posterior Probability-Projection Onto Convex Set). At first assuming the high-resolution model subject to Poisson-Markov distribution, then constructing the projecting convex based on MAP. According to the characteristics of compressed video, two different convexes are constructed based on integrating the inter-frame and intra-frame information in the wavelet-domain. The results of the experiment demonstrate that the new method not only outperforms the traditional algorithms on the aspects of PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Square Error) and reconstruction vision effect, but also has the advantages of rapid convergence and easy extension.展开更多
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
Video Super-Resolution （SR） reconstruction produces video sequences with High Resolution （HR） via the fusion of several Low-Resolution （LR） video frames. Traditional methods rely on the accurate estimation of su...Video Super-Resolution （SR） reconstruction produces video sequences with High Resolution （HR） via the fusion of several Low-Resolution （LR） video frames. Traditional methods rely on the accurate estimation of subpixel motion, which constrains their applicability to video sequences with relatively simple motions such as global translation. We propose an efficient iterative spatio-temporal adaptive SR reconstruction model based on Zemike Moment （ZM）, which is effective for spatial video sequences with arbitrary motion. The model uses region correlation judgment and self-adaptive threshold strategies to improve the effect and time efficiency of the ZM-based SR method. This leads to better mining of non-local self-similarity and local structural regularity, and is robust to noise and rotation. An efficient iterative curvature-based interpolation scheme is introduced to obtain the initial HR estimation of each LR video frame. Experimental results both on spatial and standard video sequences demonstrate that the proposed method outperforms existing methods in terms of both subjective visual and objective quantitative evaluations, and greatly improves the time efficiency.展开更多
Space-time video super-resolution(STVSR)serves the purpose to reconstruct high-resolution high-frame-rate videos from their low-resolution low-frame-rate counterparts.Recent approaches utilize end-to-end deep learning...Space-time video super-resolution(STVSR)serves the purpose to reconstruct high-resolution high-frame-rate videos from their low-resolution low-frame-rate counterparts.Recent approaches utilize end-to-end deep learning models to achieve STVSR.They first interpolate intermediate frame features between given frames,then perform local and global refinement among the feature sequence,and finally increase the spatial resolutions of these features.However,in the most important feature interpolation phase,they only capture spatial-temporal information from the most adjacent frame features,ignoring modelling long-term spatial-temporal correlations between multiple neighbouring frames to restore variable-speed object movements and maintain long-term motion continuity.In this paper,we propose a novel long-term temporal feature aggregation network(LTFA-Net)for STVSR.Specifically,we design a long-term mixture of experts(LTMoE)module for feature interpolation.LTMoE contains multiple experts to extract mutual and complementary spatial-temporal information from multiple consecutive adjacent frame features,which are then combined with different weights to obtain interpolation results using several gating nets.Next,we perform local and global feature refinement using the Locally-temporal Feature Comparison(LFC)module and bidirectional deformable ConvLSTM layer,respectively.Experimental results on two standard benchmarks,Adobe240 and GoPro,indicate the effectiveness and superiority of our approach over state of the art.展开更多
The rapid development of super-resolution microscopy has made it possible to observe subcellular structures and dynamic behaviors in living cells with nanoscale spatial resolution, greatly advancing progress in life s...The rapid development of super-resolution microscopy has made it possible to observe subcellular structures and dynamic behaviors in living cells with nanoscale spatial resolution, greatly advancing progress in life sciences. As hardware technology continues to evolve, the availability of new fluorescent probes with superior performance is becoming increasingly important. In recent years, fluorescent nanoprobes (FNPs) have emerged as highly promising fluorescent probes for bioimaging due to their high brightness and excellent photostability. This paper focuses on the development and applications of FNPs as probes for live-cell super-resolution imaging. It provides an overview of different super-resolution methods, discusses the performance requirements for FNPs in these methods, and reviews the latest applications of FNPs in the super-resolution imaging of living cells. Finally, it addresses the challenges and future outlook in this field.展开更多
Super-Resolution (SR) technique means to reconstruct High-Resolution (HR) images from a sequence of Low-Resolution (LR) observations,which has been a great focus for compressed video. Based on the theory of Projection...Super-Resolution (SR) technique means to reconstruct High-Resolution (HR) images from a sequence of Low-Resolution (LR) observations,which has been a great focus for compressed video. Based on the theory of Projection Onto Convex Set (POCS),this paper constructs Quantization Constraint Set (QCS) using the quantization information extracted from the video bit stream. By combining the statistical properties of image and the Human Visual System (HVS),a novel Adaptive Quantization Constraint Set (AQCS) is proposed. Simulation results show that AQCS-based SR al-gorithm converges at a fast rate and obtains better performance in both objective and subjective quality,which is applicable for compressed video.展开更多
Video inpainting is a technique that fills in the missing regions or gaps in a video by using its known pixels.The existing video inpainting algorithms are computationally expensive and introduce seam in the target re...Video inpainting is a technique that fills in the missing regions or gaps in a video by using its known pixels.The existing video inpainting algorithms are computationally expensive and introduce seam in the target region that arises due to variation in brightness or contrast of the patches.To overcome these drawbacks,the authors propose a novel two-stage framework.In the first step,sub-bands of wavelets of a low-resolution image are obtained using the dualtree complex wavelet transform.Criminisi algorithm and auto-regression technique are then applied to these subbands to inpaint the missing regions.The fuzzy logic-based histogram equalisation is used to further enhance the image by preserving the image brightness and improve the local contrast.In the second step,the image is enhanced using super-resolution technique.The process of down-sampling,inpainting and subsequently enhancing the video using the super-resolution technique reduces the video inpainting time.The framework is tested on video sequences by comparing and analysing the structural similarity index matrix,peak-signal-to-noise ratio,visual information fidelity in pixel domain and execution time with the state-of-the-art algorithms.The experimental analysis gives visually pleasing results for object removal and error concealment.展开更多
Existing learning-based super-resolution （SR） reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames. Aiming at applying the advantages of ...Existing learning-based super-resolution （SR） reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames. Aiming at applying the advantages of learning-based algorithms to video SR field, a novel video SR reconstruction algorithm based on deep convolutional neural network （CNN） and spatio-temporal similarity （STCNN-SR） was proposed in this paper. It is a deep learning method for video SR reconstruction, which considers not onlv the mapping relationship among associated low-resolution （LR） and high-resolution （HR） image blocks, but also the spatio-temporal non-local complementary and redundant information between adjacent low-resolution video frames. The reconstruction speed can be improved obviously with the pre-trained end-to-end reconstructed coefficients. Moreover, the performance of video SR will be further improved by the optimization process with spatio-temporal similarity. Experimental results demonstrated that the proposed algorithm achieves a competitive SR quality on both subjective and objective evaluations, when compared to other state-of-the-art algorithms.展开更多
Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual...Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual perception,significantly increasing the utility of low-resolution images.In this study,an improved image superresolution reconstruction model based on Generative Adversarial Networks(SRGAN)was proposed.This model introduced a channel and spatial attention mechanism(CSAB)in the generator,allowing it to effectively leverage the information from the input image to enhance feature representations and capture important details.The discriminator was designed with an improved PatchGAN architecture,which more accurately captured local details and texture information of the image.With these enhanced generator and discriminator architectures and an optimized loss function design,this method demonstrated superior performance in image quality assessment metrics.Experimental results showed that this model outperforms traditional methods,presenting more detailed and realistic image details in the visual effects.展开更多
Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have e...Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.展开更多
Recent Super-Resolution(SR)algorithms often suffer from excessive model complexity,high computational costs,and limited flexibility across varying image scales.To address these challenges,we propose DDNet,a dynamic an...Recent Super-Resolution(SR)algorithms often suffer from excessive model complexity,high computational costs,and limited flexibility across varying image scales.To address these challenges,we propose DDNet,a dynamic and lightweight SR framework designed for arbitrary scaling factors.DDNet integrates a residual learning structure with an Adaptively fusion Feature Block(AFB)and a scale-aware upsampling module,effectively reducing parameter overhead while preserving reconstruction quality.Additionally,we introduce DDNetGAN,an enhanced variant that leverages a relativistic Generative Adversarial Network(GAN)to further improve texture realism.To validate the proposed models,we conduct extensive training using the DIV2K and Flickr2K datasets and evaluate performance across standard benchmarks including Set5,Set14,Urban100,Manga109,and BSD100.Our experiments cover both symmetric and asymmetric upscaling factors and incorporate ablation studies to assess key components.Results show that DDNet and DDNetGAN achieve competitive performance compared with mainstream SR algorithms,demonstrating a strong balance between accuracy,efficiency,and flexibility.These findings highlight the potential of our approach for practical real-world super-resolution applications.展开更多
Blood cells are the most integral part of the body,which are made up of erythrocytes,platelets and white blood cells.The examination of subcellular structures and proteins within blood cells at the nanoscale can provi...Blood cells are the most integral part of the body,which are made up of erythrocytes,platelets and white blood cells.The examination of subcellular structures and proteins within blood cells at the nanoscale can provide valuable insights into the health status of an individual,accurate diagnosis,and efficient treatment strategies for diseases.Super-resolution microscopy(SRM)has recently emerged as a cutting-edge tool for the study of blood cells,providing numerous advantages over traditional methods for examining subcellular structures and proteins.In this paper,we focus on outlining the fundamental principles of various SRM techniques and their applications in both normal and diseased states of blood cells.Furthermore,future prospects of SRM techniques in the analysis of blood cells are also discussed.展开更多
A new scheme of super-resolution optical fluctuation imaging(SOFI)is proposed to broaden its application in the high-order cumulant reconstruction by optimizing blinking characteristics,eliminating noise in raw data a...A new scheme of super-resolution optical fluctuation imaging(SOFI)is proposed to broaden its application in the high-order cumulant reconstruction by optimizing blinking characteristics,eliminating noise in raw data and applying multi-resolution analysis in cumulant reconstruction.A motor-driven rotating mask optical modulation system is designed to adjust the excitation lightfield and allows for fast deployment.Active-modulated fluorescence fluctuation superresolution microscopy with multi-resolution analysis(AMF-MRA-SOFI)demonstrates enhanced resolution ability and reconstruction quality in experiments performed on sample of conventional dyes,achieving a resolution of 100 nm in the fourth order compared to conventional SOFI reconstruction.Furthermore,our approach combining expansion super-resolution achieved a resolution at-57 nm.展开更多
The evaluation of adsorption states and shale gas content in shale fractures and pores relies on the analysis of these fractures and pores.Scanning electron microscopy images are commonly used for shale analysis;howev...The evaluation of adsorption states and shale gas content in shale fractures and pores relies on the analysis of these fractures and pores.Scanning electron microscopy images are commonly used for shale analysis;however,their low resolution,particularly the loss of high-frequency information at pore edges,presents challenges in analyzing fractures and pores in shale gas reservoirs.This study introduced a novel neural network called the spatial-spectral domain attention network(SSDAN),which employed spatial and spectral domain attention mechanisms to extract features and restore information in parallel.The network generated super-resolution images through a fusion module that included CNN-based spatial blocks for pixel-level image information recovery,spectral blocks to process Fourier transform information of images and enhance high-frequency recovery,and an adaptive vision transformer to process Fourier transform block information,eliminating the need for a preset image size.The SSDAN model demonstrated exceptional performance in comparative experiments on marine shale and marine continental shale datasets,achieving optimal performance on key indicators such as peak signal-to-noise ratio,structural similarity,learned perceptual image patch similarity,and Frechet inception distance while also exhibiting superior visual performance in pore recovery.Ablation experiments further confirmed the effectiveness of the spatial blocks,channel attention,spectral blocks,and frequency loss function in the model.The SSDAN model showed remarkable capability in enhancing the resolution of shale gas reservoir images and restoring high-frequency information at pore edges,thereby validating its effectiveness in unconventional natural gas reservoir analyses.展开更多
Infrared imaging technology has been widely adopted in various fields,such as military reconnaissance,medical diagnosis,and security monitoring,due to its excellent ability to penetrate smoke and fog.However,the preva...Infrared imaging technology has been widely adopted in various fields,such as military reconnaissance,medical diagnosis,and security monitoring,due to its excellent ability to penetrate smoke and fog.However,the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents.In addition,deploying super-resolution models on resource-constrained devices faces significant challenges.To address these issues,this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism.The network’s dynamic weighting module automatically adjusts the weights of the attention and nonattention branch outputs based on the network’s characteristics at different levels.Among them,the attention branch is further subdivided into pixel attention and brightness-texture attention,which are specialized for extracting the most informative features in infrared images.Meanwhile,the non-attention branch supplements the extraction of those neglected features to enhance the comprehensiveness of the features.Through ablation experiments,we verify the effectiveness of the proposed module.Finally,through experiments on two datasets,FLIR and Thermal101,qualitative and quantitative results demonstrate that the model can effectively recover high-frequency details of infrared images and significantly improve image resolution.In detail,compared with the suboptimal method,we have reduced the number of parameters by 30%and improved the model performance.When the scale factor is 2,the peak signal-tonoise ratio of the test datasets FLIR and Thermal101 is improved by 0.09 and 0.15 dB,respectively.When the scale factor is 4,it is improved by 0.05 and 0.09 dB,respectively.In addition,due to the lightweight design of the network structure,it has a low computational cost.It is suitable for deployment on edge devices,thus effectively enhancing the sensing performance of infrared imaging devices.展开更多
BACKGROUND Deep learning-based super-resolution(SR)reconstruction can obtain high-quality images with more detailed information.AIM To compare multiparametric normal-resolution(NR)and SR magnetic resonance imaging(MRI...BACKGROUND Deep learning-based super-resolution(SR)reconstruction can obtain high-quality images with more detailed information.AIM To compare multiparametric normal-resolution(NR)and SR magnetic resonance imaging(MRI)in predicting the histopathologic grade in hepatocellular carcinoma.METHODS We retrospectively analyzed a total of 826 patients from two medical centers(training 459;validation 196;test 171).T2-weighted imaging,diffusion-weighted imaging,and portal venous phases were collected.Tumor segmentations were conducted automatically by 3D U-Net.Based on generative adversarial network,we utilized 3D SR reconstruction to produce SR MRI.Radiomics models were developed and validated by XGBoost and Catboost.The predictive efficiency was demonstrated by calibration curves,decision curve analysis,area under the curve(AUC)and net reclassification index(NRI).RESULTS We extracted 3045 radiomic features from both NR and SR MRI,retaining 29 and 28 features,respectively.For XGBoost models,SR MRI yielded higher AUC value than NR MRI in the validation and test cohorts(0.83 vs 0.79;0.80 vs 0.78),respectively.Consistent trends were seen in CatBoost models:SR MRI achieved AUCs of 0.89 and 0.80 compared to NR MRI’s 0.81 and 0.76.NRI indicated that the SR MRI models could improve the prediction accuracy by-1.6%to 20.9%compared to the NR MRI models.CONCLUSION Deep learning-based SR MRI could improve the predictive performance of histopathologic grade in HCC.It may be a powerful tool for better stratification management for patients with operable HCC.展开更多
Fluorogen-activating proteins(FAPs)selectively bind to specific fluorophores,inducing fluorescence activation through the inhibition of torsion of fluorophores.This binding-activation mechanism provides a highly speci...Fluorogen-activating proteins(FAPs)selectively bind to specific fluorophores,inducing fluorescence activation through the inhibition of torsion of fluorophores.This binding-activation mechanism provides a highly specific and efficient fluorescence system that minimizes background signals,significantly enhancing the signal-to-noise ratio(SNR)and making it a powerful tool in live-cell imaging.The principle of binding-activation fluorescence is fundamental to point accumulation for imaging in nanoscale topography(PAINT)super-resolution imaging.However,the high binding affinity between traditional FAPfluorophore pairs limits their application in PAINT,thus hindering the rapid and dynamic imaging necessary for high-resolution cellular studies.In this work,we designed malachite green(MG)derivatives with bulky N-substituents to modulate the binding affinity of the MG-d L5^(**)fluorophore-FAP pair.This modification introduces steric hindrance in MG-dL5^(**)system,resulting in reduced binding affinity and practicability for fast,high-resolution PAINT imaging.Among the synthesized derivatives,MG-Pen showed optimal properties,enabling rapid and high-resolution PAINT imaging of dL5^(**)in living cells.This study highlights the potential of MG derivatives optimization in overcoming the limitations of fluorophore-FAP pairs for super-resolution imaging and provides a new approach for enhancing the performance of PAINT in living cell applications.展开更多
Neutron capture event imaging is a novel technique that has the potential to substantially enhance the resolution of existing imaging systems.This study provides a measurement method for neutron capture event distribu...Neutron capture event imaging is a novel technique that has the potential to substantially enhance the resolution of existing imaging systems.This study provides a measurement method for neutron capture event distribution along with multiple reconstruction methods for super-resolution imaging.The proposed technology reduces the point-spread function of an imag-ing system through single-neutron detection and event reconstruction,thereby significantly improving imaging resolution.A single-neutron detection experiment was conducted using a highly practical and efficient^(6)LiF-ZnS scintillation screen of a cold neutron imaging device in the research reactor.In milliseconds of exposure time,a large number of weak light clusters and their distribution in the scintillation screen were recorded frame by frame,to complete single-neutron detection.Several reconstruction algorithms were proposed for the calculations.The location of neutron capture was calculated using several processing methods such as noise removal,filtering,spot segmentation,contour analysis,and local positioning.The proposed algorithm achieved a higher imaging resolution and faster reconstruction speed,and single-neutron super-resolution imaging was realized by combining single-neutron detection experiments and reconstruction calculations.The results show that the resolution of the 100μm thick^(6)LiF-ZnS scintillation screen can be improved from 125 to 40 microns.This indicates that the proposed single-neutron detection and calculation method is effective and can significantly improve imaging resolution.展开更多
基金supported in part by the Basic and Applied Basic Research Foundation of Guangdong Province[2025A1515011566]in part by the State Key Laboratory for Novel Software Technology,Nanjing University[KFKT2024B08]+1 种基金in part by Leading Talents in Gusu Innovation and Entrepreneurship[ZXL2023170]in part by the Basic Research Programs of Taicang 2024,[TC2024JC32].
文摘Deep convolutional neural networks(CNNs)have demonstrated remarkable performance in video super-resolution(VSR).However,the ability of most existing methods to recover fine details in complex scenes is often hindered by the loss of shallow texture information during feature extraction.To address this limitation,we propose a 3D Convolutional Enhanced Residual Video Super-Resolution Network(3D-ERVSNet).This network employs a forward and backward bidirectional propagation module(FBBPM)that aligns features across frames using explicit optical flow through lightweight SPyNet.By incorporating an enhanced residual structure(ERS)with skip connections,shallow and deep features are effectively integrated,enhancing texture restoration capabilities.Furthermore,3D convolution module(3DCM)is applied after the backward propagation module to implicitly capture spatio-temporal dependencies.The architecture synergizes these components where FBBPM extracts aligned features,ERS fuses hierarchical representations,and 3DCM refines temporal coherence.Finally,a deep feature aggregation module(DFAM)fuses the processed features,and a pixel-upsampling module(PUM)reconstructs the high-resolution(HR)video frames.Comprehensive evaluations on REDS,Vid4,UDM10,and Vim4 benchmarks demonstrate well performance including 30.95 dB PSNR/0.8822 SSIM on REDS and 32.78 dB/0.8987 on Vim4.3D-ERVSNet achieves significant gains over baselines while maintaining high efficiency with only 6.3M parameters and 77ms/frame runtime(i.e.,20×faster than RBPN).The network’s effectiveness stems from its task-specific asymmetric design that balances explicit alignment and implicit fusion.
基金Supported by Open Project of the Ministry of Industry and Information Technology Key Laboratory of Performance and Reliability Testing and Evaluation for Basic Software and Hardware。
文摘Background Recurrent recovery is a common method for video super-resolution(VSR)that models the correlation between frames via hidden states.However,the application of this structure in real-world scenarios can lead to unsatisfactory artifacts.We found that in real-world VSR training,the use of unknown and complex degradation can better simulate the degradation process in the real world.Methods Based on this,we propose the RealFuVSR model,which simulates real-world degradation and mitigates artifacts caused by the VSR.Specifically,we propose a multiscale feature extraction module(MSF)module that extracts and fuses features from multiple scales,thereby facilitating the elimination of hidden state artifacts.To improve the accuracy of the hidden state alignment information,RealFuVSR uses an advanced optical flow-guided deformable convolution.Moreover,a cascaded residual upsampling module was used to eliminate noise caused by the upsampling process.Results The experiment demonstrates that RealFuVSR model can not only recover high-quality videos but also outperforms the state-of-the-art RealBasicVSR and RealESRGAN models.
基金Supported by the Natural Science Foundation of Jiangsu Province (No. BK2004151).
文摘This letter proposes a novel method of compressed video super-resolution reconstruction based on MAP-POCS (Maximum Posterior Probability-Projection Onto Convex Set). At first assuming the high-resolution model subject to Poisson-Markov distribution, then constructing the projecting convex based on MAP. According to the characteristics of compressed video, two different convexes are constructed based on integrating the inter-frame and intra-frame information in the wavelet-domain. The results of the experiment demonstrate that the new method not only outperforms the traditional algorithms on the aspects of PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Square Error) and reconstruction vision effect, but also has the advantages of rapid convergence and easy extension.
基金This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
基金the National Basic Research Program of China (973 Program) under Grant No.2012CB821200，the National Natural Science Foundation of China under Grants No.91024001,No.61070142，the Beijing Natural Science Foundation under Grant No.4111002
文摘Video Super-Resolution （SR） reconstruction produces video sequences with High Resolution （HR） via the fusion of several Low-Resolution （LR） video frames. Traditional methods rely on the accurate estimation of subpixel motion, which constrains their applicability to video sequences with relatively simple motions such as global translation. We propose an efficient iterative spatio-temporal adaptive SR reconstruction model based on Zemike Moment （ZM）, which is effective for spatial video sequences with arbitrary motion. The model uses region correlation judgment and self-adaptive threshold strategies to improve the effect and time efficiency of the ZM-based SR method. This leads to better mining of non-local self-similarity and local structural regularity, and is robust to noise and rotation. An efficient iterative curvature-based interpolation scheme is introduced to obtain the initial HR estimation of each LR video frame. Experimental results both on spatial and standard video sequences demonstrate that the proposed method outperforms existing methods in terms of both subjective visual and objective quantitative evaluations, and greatly improves the time efficiency.
文摘Space-time video super-resolution(STVSR)serves the purpose to reconstruct high-resolution high-frame-rate videos from their low-resolution low-frame-rate counterparts.Recent approaches utilize end-to-end deep learning models to achieve STVSR.They first interpolate intermediate frame features between given frames,then perform local and global refinement among the feature sequence,and finally increase the spatial resolutions of these features.However,in the most important feature interpolation phase,they only capture spatial-temporal information from the most adjacent frame features,ignoring modelling long-term spatial-temporal correlations between multiple neighbouring frames to restore variable-speed object movements and maintain long-term motion continuity.In this paper,we propose a novel long-term temporal feature aggregation network(LTFA-Net)for STVSR.Specifically,we design a long-term mixture of experts(LTMoE)module for feature interpolation.LTMoE contains multiple experts to extract mutual and complementary spatial-temporal information from multiple consecutive adjacent frame features,which are then combined with different weights to obtain interpolation results using several gating nets.Next,we perform local and global feature refinement using the Locally-temporal Feature Comparison(LFC)module and bidirectional deformable ConvLSTM layer,respectively.Experimental results on two standard benchmarks,Adobe240 and GoPro,indicate the effectiveness and superiority of our approach over state of the art.
基金supported by the following grants:National Natural Science Foundation of China(grant nos.92354305,32271428,and 32201132)National Key R&D Program of China(grant no.2022YFC3401100)+1 种基金Fund for Knowledge Innovation of Wuhan Science and Technology Bureau(grant no.2022020801010558)Director Fund of WNLO.
文摘The rapid development of super-resolution microscopy has made it possible to observe subcellular structures and dynamic behaviors in living cells with nanoscale spatial resolution, greatly advancing progress in life sciences. As hardware technology continues to evolve, the availability of new fluorescent probes with superior performance is becoming increasingly important. In recent years, fluorescent nanoprobes (FNPs) have emerged as highly promising fluorescent probes for bioimaging due to their high brightness and excellent photostability. This paper focuses on the development and applications of FNPs as probes for live-cell super-resolution imaging. It provides an overview of different super-resolution methods, discusses the performance requirements for FNPs in these methods, and reviews the latest applications of FNPs in the super-resolution imaging of living cells. Finally, it addresses the challenges and future outlook in this field.
基金the Natural Science Foundation of Jiangsu Province (No.BK2004151).
文摘Super-Resolution (SR) technique means to reconstruct High-Resolution (HR) images from a sequence of Low-Resolution (LR) observations,which has been a great focus for compressed video. Based on the theory of Projection Onto Convex Set (POCS),this paper constructs Quantization Constraint Set (QCS) using the quantization information extracted from the video bit stream. By combining the statistical properties of image and the Human Visual System (HVS),a novel Adaptive Quantization Constraint Set (AQCS) is proposed. Simulation results show that AQCS-based SR al-gorithm converges at a fast rate and obtains better performance in both objective and subjective quality,which is applicable for compressed video.
文摘Video inpainting is a technique that fills in the missing regions or gaps in a video by using its known pixels.The existing video inpainting algorithms are computationally expensive and introduce seam in the target region that arises due to variation in brightness or contrast of the patches.To overcome these drawbacks,the authors propose a novel two-stage framework.In the first step,sub-bands of wavelets of a low-resolution image are obtained using the dualtree complex wavelet transform.Criminisi algorithm and auto-regression technique are then applied to these subbands to inpaint the missing regions.The fuzzy logic-based histogram equalisation is used to further enhance the image by preserving the image brightness and improve the local contrast.In the second step,the image is enhanced using super-resolution technique.The process of down-sampling,inpainting and subsequently enhancing the video using the super-resolution technique reduces the video inpainting time.The framework is tested on video sequences by comparing and analysing the structural similarity index matrix,peak-signal-to-noise ratio,visual information fidelity in pixel domain and execution time with the state-of-the-art algorithms.The experimental analysis gives visually pleasing results for object removal and error concealment.
基金supported by the National Natural Science Foundation of China (61320106006, 61532006, 61502042)
文摘Existing learning-based super-resolution （SR） reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames. Aiming at applying the advantages of learning-based algorithms to video SR field, a novel video SR reconstruction algorithm based on deep convolutional neural network （CNN） and spatio-temporal similarity （STCNN-SR） was proposed in this paper. It is a deep learning method for video SR reconstruction, which considers not onlv the mapping relationship among associated low-resolution （LR） and high-resolution （HR） image blocks, but also the spatio-temporal non-local complementary and redundant information between adjacent low-resolution video frames. The reconstruction speed can be improved obviously with the pre-trained end-to-end reconstructed coefficients. Moreover, the performance of video SR will be further improved by the optimization process with spatio-temporal similarity. Experimental results demonstrated that the proposed algorithm achieves a competitive SR quality on both subjective and objective evaluations, when compared to other state-of-the-art algorithms.
文摘Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual perception,significantly increasing the utility of low-resolution images.In this study,an improved image superresolution reconstruction model based on Generative Adversarial Networks(SRGAN)was proposed.This model introduced a channel and spatial attention mechanism(CSAB)in the generator,allowing it to effectively leverage the information from the input image to enhance feature representations and capture important details.The discriminator was designed with an improved PatchGAN architecture,which more accurately captured local details and texture information of the image.With these enhanced generator and discriminator architectures and an optimized loss function design,this method demonstrated superior performance in image quality assessment metrics.Experimental results showed that this model outperforms traditional methods,presenting more detailed and realistic image details in the visual effects.
文摘Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.
基金supported by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004].
文摘Recent Super-Resolution(SR)algorithms often suffer from excessive model complexity,high computational costs,and limited flexibility across varying image scales.To address these challenges,we propose DDNet,a dynamic and lightweight SR framework designed for arbitrary scaling factors.DDNet integrates a residual learning structure with an Adaptively fusion Feature Block(AFB)and a scale-aware upsampling module,effectively reducing parameter overhead while preserving reconstruction quality.Additionally,we introduce DDNetGAN,an enhanced variant that leverages a relativistic Generative Adversarial Network(GAN)to further improve texture realism.To validate the proposed models,we conduct extensive training using the DIV2K and Flickr2K datasets and evaluate performance across standard benchmarks including Set5,Set14,Urban100,Manga109,and BSD100.Our experiments cover both symmetric and asymmetric upscaling factors and incorporate ablation studies to assess key components.Results show that DDNet and DDNetGAN achieve competitive performance compared with mainstream SR algorithms,demonstrating a strong balance between accuracy,efficiency,and flexibility.These findings highlight the potential of our approach for practical real-world super-resolution applications.
基金supported by the following grants:National Key R&D Program of China(Grant no.2022YFC3401100)National Natural Science Foundation of China(Grant nos.32271428,92054110,32201132 and 31600692).
文摘Blood cells are the most integral part of the body,which are made up of erythrocytes,platelets and white blood cells.The examination of subcellular structures and proteins within blood cells at the nanoscale can provide valuable insights into the health status of an individual,accurate diagnosis,and efficient treatment strategies for diseases.Super-resolution microscopy(SRM)has recently emerged as a cutting-edge tool for the study of blood cells,providing numerous advantages over traditional methods for examining subcellular structures and proteins.In this paper,we focus on outlining the fundamental principles of various SRM techniques and their applications in both normal and diseased states of blood cells.Furthermore,future prospects of SRM techniques in the analysis of blood cells are also discussed.
基金supported by the National Natural Science Foundation of China(62175034,62175036,32271510)the National Key R&D Program of China(2021YFF0502900)+2 种基金the Science and Technology Research Program of Shanghai(Grant No.19DZ2282100)the Shanghai Key Laboratory of Metasurfaces for Light Manipulation(23dz2260100)the Shanghai Engineering Technology Research Center of Hair Medicine(19DZ2250500).
文摘A new scheme of super-resolution optical fluctuation imaging(SOFI)is proposed to broaden its application in the high-order cumulant reconstruction by optimizing blinking characteristics,eliminating noise in raw data and applying multi-resolution analysis in cumulant reconstruction.A motor-driven rotating mask optical modulation system is designed to adjust the excitation lightfield and allows for fast deployment.Active-modulated fluorescence fluctuation superresolution microscopy with multi-resolution analysis(AMF-MRA-SOFI)demonstrates enhanced resolution ability and reconstruction quality in experiments performed on sample of conventional dyes,achieving a resolution of 100 nm in the fourth order compared to conventional SOFI reconstruction.Furthermore,our approach combining expansion super-resolution achieved a resolution at-57 nm.
基金the National Natural Science Foundation(NNSF)of China under Grant 41927801.
文摘The evaluation of adsorption states and shale gas content in shale fractures and pores relies on the analysis of these fractures and pores.Scanning electron microscopy images are commonly used for shale analysis;however,their low resolution,particularly the loss of high-frequency information at pore edges,presents challenges in analyzing fractures and pores in shale gas reservoirs.This study introduced a novel neural network called the spatial-spectral domain attention network(SSDAN),which employed spatial and spectral domain attention mechanisms to extract features and restore information in parallel.The network generated super-resolution images through a fusion module that included CNN-based spatial blocks for pixel-level image information recovery,spectral blocks to process Fourier transform information of images and enhance high-frequency recovery,and an adaptive vision transformer to process Fourier transform block information,eliminating the need for a preset image size.The SSDAN model demonstrated exceptional performance in comparative experiments on marine shale and marine continental shale datasets,achieving optimal performance on key indicators such as peak signal-to-noise ratio,structural similarity,learned perceptual image patch similarity,and Frechet inception distance while also exhibiting superior visual performance in pore recovery.Ablation experiments further confirmed the effectiveness of the spatial blocks,channel attention,spectral blocks,and frequency loss function in the model.The SSDAN model showed remarkable capability in enhancing the resolution of shale gas reservoir images and restoring high-frequency information at pore edges,thereby validating its effectiveness in unconventional natural gas reservoir analyses.
基金funded in part by theHenan ProvinceKeyR&DProgramProject,“Research and Application Demonstration of Class Ⅱ Superlattice Medium Wave High Temperature Infrared Detector Technology”under Grant No.231111210400.
文摘Infrared imaging technology has been widely adopted in various fields,such as military reconnaissance,medical diagnosis,and security monitoring,due to its excellent ability to penetrate smoke and fog.However,the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents.In addition,deploying super-resolution models on resource-constrained devices faces significant challenges.To address these issues,this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism.The network’s dynamic weighting module automatically adjusts the weights of the attention and nonattention branch outputs based on the network’s characteristics at different levels.Among them,the attention branch is further subdivided into pixel attention and brightness-texture attention,which are specialized for extracting the most informative features in infrared images.Meanwhile,the non-attention branch supplements the extraction of those neglected features to enhance the comprehensiveness of the features.Through ablation experiments,we verify the effectiveness of the proposed module.Finally,through experiments on two datasets,FLIR and Thermal101,qualitative and quantitative results demonstrate that the model can effectively recover high-frequency details of infrared images and significantly improve image resolution.In detail,compared with the suboptimal method,we have reduced the number of parameters by 30%and improved the model performance.When the scale factor is 2,the peak signal-tonoise ratio of the test datasets FLIR and Thermal101 is improved by 0.09 and 0.15 dB,respectively.When the scale factor is 4,it is improved by 0.05 and 0.09 dB,respectively.In addition,due to the lightweight design of the network structure,it has a low computational cost.It is suitable for deployment on edge devices,thus effectively enhancing the sensing performance of infrared imaging devices.
基金Supported by AI+Health Collaborative Innovation Cultivation Project of Beijing City,No.Z221100003522005.
文摘BACKGROUND Deep learning-based super-resolution(SR)reconstruction can obtain high-quality images with more detailed information.AIM To compare multiparametric normal-resolution(NR)and SR magnetic resonance imaging(MRI)in predicting the histopathologic grade in hepatocellular carcinoma.METHODS We retrospectively analyzed a total of 826 patients from two medical centers(training 459;validation 196;test 171).T2-weighted imaging,diffusion-weighted imaging,and portal venous phases were collected.Tumor segmentations were conducted automatically by 3D U-Net.Based on generative adversarial network,we utilized 3D SR reconstruction to produce SR MRI.Radiomics models were developed and validated by XGBoost and Catboost.The predictive efficiency was demonstrated by calibration curves,decision curve analysis,area under the curve(AUC)and net reclassification index(NRI).RESULTS We extracted 3045 radiomic features from both NR and SR MRI,retaining 29 and 28 features,respectively.For XGBoost models,SR MRI yielded higher AUC value than NR MRI in the validation and test cohorts(0.83 vs 0.79;0.80 vs 0.78),respectively.Consistent trends were seen in CatBoost models:SR MRI achieved AUCs of 0.89 and 0.80 compared to NR MRI’s 0.81 and 0.76.NRI indicated that the SR MRI models could improve the prediction accuracy by-1.6%to 20.9%compared to the NR MRI models.CONCLUSION Deep learning-based SR MRI could improve the predictive performance of histopathologic grade in HCC.It may be a powerful tool for better stratification management for patients with operable HCC.
基金supported by the National Natural Science Foundation of China(Nos.22225806,22078314,22278394,22378385)Dalian Institute of Chemical Physics(Nos.DICPI202142,DICPI202436)。
文摘Fluorogen-activating proteins(FAPs)selectively bind to specific fluorophores,inducing fluorescence activation through the inhibition of torsion of fluorophores.This binding-activation mechanism provides a highly specific and efficient fluorescence system that minimizes background signals,significantly enhancing the signal-to-noise ratio(SNR)and making it a powerful tool in live-cell imaging.The principle of binding-activation fluorescence is fundamental to point accumulation for imaging in nanoscale topography(PAINT)super-resolution imaging.However,the high binding affinity between traditional FAPfluorophore pairs limits their application in PAINT,thus hindering the rapid and dynamic imaging necessary for high-resolution cellular studies.In this work,we designed malachite green(MG)derivatives with bulky N-substituents to modulate the binding affinity of the MG-d L5^(**)fluorophore-FAP pair.This modification introduces steric hindrance in MG-dL5^(**)system,resulting in reduced binding affinity and practicability for fast,high-resolution PAINT imaging.Among the synthesized derivatives,MG-Pen showed optimal properties,enabling rapid and high-resolution PAINT imaging of dL5^(**)in living cells.This study highlights the potential of MG derivatives optimization in overcoming the limitations of fluorophore-FAP pairs for super-resolution imaging and provides a new approach for enhancing the performance of PAINT in living cell applications.
基金supported by the National Natural Science Foundation of China(Nos.12205271,12075217,U20B2011,and 51978218)Sichuan Science and Technology Program(No.2019ZDZX0010)the National Key R&D Program of China(No.2022YFA1604002).
文摘Neutron capture event imaging is a novel technique that has the potential to substantially enhance the resolution of existing imaging systems.This study provides a measurement method for neutron capture event distribution along with multiple reconstruction methods for super-resolution imaging.The proposed technology reduces the point-spread function of an imag-ing system through single-neutron detection and event reconstruction,thereby significantly improving imaging resolution.A single-neutron detection experiment was conducted using a highly practical and efficient^(6)LiF-ZnS scintillation screen of a cold neutron imaging device in the research reactor.In milliseconds of exposure time,a large number of weak light clusters and their distribution in the scintillation screen were recorded frame by frame,to complete single-neutron detection.Several reconstruction algorithms were proposed for the calculations.The location of neutron capture was calculated using several processing methods such as noise removal,filtering,spot segmentation,contour analysis,and local positioning.The proposed algorithm achieved a higher imaging resolution and faster reconstruction speed,and single-neutron super-resolution imaging was realized by combining single-neutron detection experiments and reconstruction calculations.The results show that the resolution of the 100μm thick^(6)LiF-ZnS scintillation screen can be improved from 125 to 40 microns.This indicates that the proposed single-neutron detection and calculation method is effective and can significantly improve imaging resolution.
