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DDNet:A Novel Dynamic Lightweight Super-Resolution Algorithm for Arbitrary Scales 认领 引用
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作者 Yiqiao Gong Chunlai Wu +4 位作者 Wenfeng Zheng Siyu Lu Guangyu Xu Lijuan Zhang Lirong Yin 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第11期2223-2252,共30页
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
关键词 DDNet DDNetGAN fully dynamic lightweight arbitrary scale super-resolution algorithm
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AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms 认领 引用 被引量：3
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作者 Lirong Yin Lei Wang +7 位作者 Siyu Lu Ruiyang Wang Haitao Ren Ahmed AlSanad Salman A.AlQahtani Zhengtong Yin Xiaolu Li Wenfeng Zheng 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第9期2315-2347,共33页
At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalizatio... At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization ability.Given this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature extraction.This module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating mechanism.Combined with dynamic convolution with scale information,the network can extract more differentiated feature information.The introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature information.Dynamic convolution and pixel-based gating mechanisms enhance the module’s adaptability.Finally,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB module.The results revealed that the network combined with the AFB module has stronger generalization ability and expression ability. 展开更多
关键词 Super-resolution feature extraction dynamic convolution attention mechanism gate control
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Fast image super-resolution algorithm based on multi-resolution dictionary learning and sparse representation 认领 引用 被引量：3
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作者 ZHAO Wei BIAN Xiaofeng +2 位作者 HUANG Fang WANG Jun ABIDI Mongi A. 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第3期471-482,共12页
Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artif... Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception. 展开更多
关键词 single image super-resolution（SR） sparse representation multi-resolution dictionary learning（MRDL） adaptive patch partition method（APPM）
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Polarimetric super-resolution algorithm for radar range imaging via spatial smoothing processing 认领 引用
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作者 LI Zhang-feng ZHAO Guo-qiang +3 位作者 LI Shi-yong LIU Fang SUN Hou-jun TAO Ran 《Journal of Beijing Institute of Technology》 EI CAS 2016年第3期397-402,共6页
A full-polarimetric super-resolution algorithm with spatial smoothing processing is presented for one-dimensional（1-D）radar imaging.The coherence between scattering centers is minimized by using spatial smoothing pr... A full-polarimetric super-resolution algorithm with spatial smoothing processing is presented for one-dimensional（1-D）radar imaging.The coherence between scattering centers is minimized by using spatial smoothing processing（SSP）.Then the range and polarimetric scattering matrix of the scattering centers are estimated.The impact of different lengths of the smoothing window on the imaging quality is mainly analyzed with different signal-to-noise ratios（SNR）.Simulation and experimental results show that an improved radar super-resolution range profile and more precise estimation can be obtained by adjusting the length of the smoothing window under different SNR conditions. 展开更多
关键词 super-resolution imaging MUSIC imaging polarimetric radar spatial smoothing processing（SSP） signal-to-noise ratio（SNR）
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Super-resolution reconstruction of UAV-borne gamma-ray spectrum images based on Real-ESRGAN algorithm 认领 引用
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作者 Xin Wang Yuan Yuan +4 位作者 Xuan Zhao Guang-Hao Luo Qi-Qiao Wei He-Xi Wu Chao Xiong 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第2期42-54,共13页
Unmanned aerial vehicle(UAV)-borne gamma-ray spectrum survey plays a crucial role in geological mapping,radioactive mineral exploration,and environmental monitoring.However,raw data are often compromised by flight and... Unmanned aerial vehicle(UAV)-borne gamma-ray spectrum survey plays a crucial role in geological mapping,radioactive mineral exploration,and environmental monitoring.However,raw data are often compromised by flight and instrument background noise,as well as detector resolution limitations,which affect the accuracy of geological interpretations.This study aims to explore the application of the Real-ESRGAN algorithm in the super-resolution reconstruction of UAV-borne gamma-ray spectrum images to enhance spatial resolution and the quality of geological feature visualization.We conducted super-resolution reconstruction experiments with 2×,4×and 6×magnification using the Real-ESRGAN algorithm,comparing the results with three other mainstream algorithms(SRCNN,SRGAN,FSRCNN)to verify the superiority in image quality.The experimental results indicate that Real-ESRGAN achieved a structural similarity index(SSIM)value of 0.950 at 2×magnification,significantly higher than the other algorithms,demonstrating its advantage in detail preservation.Furthermore,Real-ESRGAN effectively reduced ringing and overshoot artifacts,enhancing the clarity of geological structures and mineral deposit sites,thus providing high-quality visual information for geological exploration. 展开更多
关键词 UAV-borne gamma-ray spectrum Super-resolution reconstruction Real-ESRGAN Image processing
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BAID:A Lightweight Super-Resolution Network with Binary Attention-Guided Frequency-Aware Information Distillation 认领 引用
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作者 Jiajia Liu Junyi Lin +3 位作者 Wenxiang Dong Xuan Zhao Jianhua Liu Huiru Li 《Computers, Materials & Continua》 SCIE EI 2026年第2期1190-1208,共19页
Single Image Super-Resolution(SISR)seeks to reconstruct high-resolution(HR)images from lowresolution(LR)inputs,thereby enhancing visual fidelity and the perception of fine details.While Transformer-based models—such ... Single Image Super-Resolution(SISR)seeks to reconstruct high-resolution(HR)images from lowresolution(LR)inputs,thereby enhancing visual fidelity and the perception of fine details.While Transformer-based models—such as SwinIR,Restormer,and HAT—have recently achieved impressive results in super-resolution tasks by capturing global contextual information,these methods often suffer from substantial computational and memory overhead,which limits their deployment on resource-constrained edge devices.To address these challenges,we propose a novel lightweight super-resolution network,termed Binary Attention-Guided Information Distillation(BAID),which integrates frequency-aware modeling with a binary attention mechanism to significantly reduce computational complexity and parameter count whilemaintaining strong reconstruction performance.The network combines a high–low frequency decoupling strategy with a local–global attention sharing mechanism,enabling efficient compression of redundant computations through binary attention guidance.At the core of the architecture lies the Attention-Guided Distillation Block(AGDB),which retains the strengths of the information distillation framework while introducing a sparse binary attention module to enhance both inference efficiency and feature representation.Extensive×4 superresolution experiments on four standard benchmarks—Set5,Set14,BSD100,and Urban100—demonstrate that BAID achieves Peak Signal-to-Noise Ratio(PSNR)values of 32.13,28.51,27.47,and 26.15,respectively,with only 1.22 million parameters and 26.1 G Floating-Point Operations(FLOPs),outperforming other state-of-the-art lightweight methods such as Information Multi-Distillation Network(IMDN)and Residual Feature Distillation Network(RFDN).These results highlight the proposed model’s ability to deliver high-quality image reconstruction while offering strong deployment efficiency,making it well-suited for image restoration tasks in resource-limited environments. 展开更多
关键词 Single image super-resolution lightweight network binary attention information distillation
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A Super-Resolution Generative Adversarial Network for Remote Sensing Images Based on Improved Residual Module and Attention Mechanism 认领 引用
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作者 Yifan Zhang Yong Gan +1 位作者 Mengke Tang Xinxin Gan 《Computers, Materials & Continua》 SCIE EI 2026年第2期689-707,共19页
High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleim... High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft. 展开更多
关键词 Remote sensing imagery generative adversarial networks super-resolution enhanced residual unit selfattention mechanism
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An LPS-targeted fluorescent probe for dynamic super-resolution imaging of Gram-negative bacterial outer membranes 认领 引用
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作者 Yi Tao Jie Pan +2 位作者 Wenting Yin Qinglong Qiao Zhaochao Xu 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第7期494-499,共6页
The outer membrane(OM)of Gram-negative bacteria,with its unique and complex structure,serves as a critical structural foundation for their heightened pathogenicity and drug resistance.However,with a thickness of only ... The outer membrane(OM)of Gram-negative bacteria,with its unique and complex structure,serves as a critical structural foundation for their heightened pathogenicity and drug resistance.However,with a thickness of only 7-9 nm,the OM is highly fragile and undergoes continuous remodeling,making it difficult to elucidate its structural dynamics and functional mechanisms during bacterial growth,division,and host invasion.Here,we developed Rho-PMXB as an OM-specific fluorescent probe that incorporates a polymyxin-derived moiety for highly specific recognition of OM lipopolysaccharides(LPS)and a rhodamine fluorophore that confers excellent photostability and high fluorescence brightness.Rho-PMXB enables selective labeling of the OMs of various Gram-negative bacteria and supports super-resolution imaging,allowing real-time visualization of OM dynamics during bacterial growth,division,and antibiotic treatment,thereby providing insight into antimicrobial mechanisms.Furthermore,Rho-PMXB enables dynamic super-resolution imaging of pathogen-host cell interactions,visualizing OM behavior during Porphyromonas gingivalis invasion of human gingival epithelial(HGE)cells and revealing its interplay with the host plasma membrane and actin cytoskeleton. 展开更多
关键词 LPS targeting Fluorescent probe Rhodamine Gram-negative bacterial outer membrane Dynamic super-resolution imaging
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A Surrogate Deep-Learning Super-Resolution Framework for Accelerating Finite Element Method-Based Fluid Simulations 认领 引用
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作者 Sojin Shin Guk Heon Kim +1 位作者 Seung Hwan Kim Jaemin Kim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期593-613,共21页
This study develops a surrogate super-resolution(SR)framework that accelerates finite element method(FEM)-based computational fluid dynamics(CFD)using deep learning.High-resolution(HR)FEM-based CFDremains computationa... This study develops a surrogate super-resolution(SR)framework that accelerates finite element method(FEM)-based computational fluid dynamics(CFD)using deep learning.High-resolution(HR)FEM-based CFDremains computationally prohibitive for time-sensitive applications,including patient-specific aneurysm hemodynamics where rapid turnaround is valuable.The proposed pipeline learns to reconstruct HR velocity-magnitude fields fromlow-resolution(LR)FEM solutions generated under the same governing equations and boundary conditions.It consistsof three modules:(i)offline pre-training of a residual network on representative vascular geometries;(ii)lightweightfine-tuning to adapt the pretrained model to geometric variability,including patient-specific aneurysm morphologies;and(iii)an unstructured-to-structured sampling strategy with region-of-interest upsampling that concentrates resolution in flow-critical zones(e.g.,the aneurysm sac)rather than the full domain.This targeted reconstruction substantiallyreduces inference and post-processing cost while preserving key HR flow features.Experiments on cerebral aneurysmmodels show that HR velocity-magnitude fields can be recovered with accuracy comparable to direct HR simulationsat less than 1%of the direct HR simulation cost per analysis(LR simulation and SR inference),while adaptation to newgeometries requires only lightweight fine-tuning with limited target-specific HR data.While clinical endpoints andadditional variables(e.g.,pressure or wall-based metrics)are left for future work,the results indicate that the proposedsurrogate SR approach can streamline FEM-based CFD workflows toward near real-time hemodynamic analysis acrossmorphologically similar vascular models. 展开更多
关键词 Surrogate modeling deep learning super-resolution finite element method(FEM) fluid simulation
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Multi-Constraint Generative Adversarial Network-Driven Optimization Method for Super-Resolution Reconstruction of Remote Sensing Images 认领 引用
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作者 Binghong Zhang Jialing Zhou +3 位作者 Xinye Zhou Jia Zhao Jinchun Zhu Guangpeng Fan 《Computers, Materials & Continua》 SCIE EI 2026年第1期779-796,共18页
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
关键词 Charbonnier loss function deep learning generative adversarial network perceptual loss remote sensing image super-resolution
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High-precision calibration of binocular camera with super-resolution technology 认领 引用
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作者 SUN Ting MA Chao +2 位作者 SUN Tian PEI Shanshan LONG Qian 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期844-860,共17页
The calibration of parameters for onboard stereo cameras is crucial for achieving efficient visual-assisted driving.However,in practical scenarios,low-resolution images often result in inaccuracies in feature point ex... The calibration of parameters for onboard stereo cameras is crucial for achieving efficient visual-assisted driving.However,in practical scenarios,low-resolution images often result in inaccuracies in feature point extraction,thereby affecting the accuracy of camera parameter calibration.To address this issue,this paper proposes a self-calibration method for stereo cameras based on joint de-noising,de-mosaic,de-ringing,and super-resolution network(JDDDSN)super-resolution reconstruction.By reconstructing images into higher-resolution images with richer details,feature points are extracted for extrinsic calibration of stereo cameras.For real-world driving scenarios,the reconstructed images achieve noise and ringing artifact reduction while obtaining clearer high-resolution images.This study further investigates the impact of the super-resolution reconstruction network on target area calibration at various distances.Additionally,it highlights the significant role of super-resolution in enhancing stereo camera calibration accuracy by removing dynamic points and focusing on static regions.Through a series of experiments,this paper validates the effectiveness and practicality of the JDDDSN super-resolution reconstruction network in improving stereo camera calibration accuracy,demonstrating its application value in the field of stereoscopic vision. 展开更多
关键词 super-resolution reconstruction camera calibration self-calibration binocular camera
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Efficient Channel Estimation for Millimeter Wave and Terahertz Systems Enabled by Integrated Super-Resolution Sensing and Communication 认领 引用
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作者 Xu Jingran Wang Huizhi +5 位作者 Zeng Yong Xu Xiaoli Wu Qingqing Yang Fei Chen Yan Abbas Jamalipour 《China Communications》 SCIE EI CSCD 2026年第6期31-49,共19页
Integrated super-resolution sensing and communication(ISSAC)has emerged as a promising technology to achieve extremely high precision sensing for those key parameters,such as the angles of the sensing targets.In this ... Integrated super-resolution sensing and communication(ISSAC)has emerged as a promising technology to achieve extremely high precision sensing for those key parameters,such as the angles of the sensing targets.In this paper,we propose an efficient channel estimation scheme enabled by ISSAC for millimeter wave(mmWave)and terahertz(THz)systems with a hybrid analog/digital beamforming architecture,where both the pilot overhead and the cost of radio frequency(RF)chains are significantly reduced.The key idea is to exploit the fact that subspace-based super-resolution algorithms such as multiple signal classification(MUSIC)can estimate channel parameters accurately without requiring dedicated a priori known pilots.In particular,the proposed method consists of two stages.First,the angles of the multi-path channel components are estimated in a pilot-free manner during the transmission of data symbols.For hybrid beamforming structure,two approaches are introduced to obtain spatial covariance matrix in this stage.Second,the multi-path channel coefficients are estimated with very few pilots.Compared to conventional channel estimation schemes that rely solely on channel training,our approach requires estimating much fewer parameters in the second stage.Furthermore,with channel multi-path angles obtained,the hybrid beamforming gain can be achieved when pilots are sent to estimate the channel path gains.To comprehensively investigate the performance of the proposed scheme,we consider both the basic line-ofsight(LoS)channels and more general multi-path channels.We compare the performance of the mean square error(MSE)of channel estimation and the resulting hybrid beamforming gains of our proposed scheme with the traditional scheme that rely exclusively on channel training.It is demonstrated that our proposed method significantly outperforms the benchmarking scheme.Simulation results are presented to validate our theoretical findings. 展开更多
关键词 channel estimation few pilots hybrid beamforming integrated super-resolution sensing and communication(ISSAC)
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Super-resolution optical frequency domain reflectometry based on spectral phase shift convolution 认领 引用
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作者 Kaijun Liu Guolu Yin +2 位作者 Junping Zhang Liuxi Chen Tao Zhu 《Advanced Photonics Nexus》 CSCD 2026年第4期71-79,共9页
For the traditional optical frequency domain reflectometry(OFDR)technology based on the crosscorrelation demodulation algorithm,there is a mutual limitation between its sensing spatial resolution and sensing accuracy.... For the traditional optical frequency domain reflectometry(OFDR)technology based on the crosscorrelation demodulation algorithm,there is a mutual limitation between its sensing spatial resolution and sensing accuracy.This restriction makes it challenging to achieve resolutions at the micron scale.We propose a spectral phase shift convolution demodulation scheme for OFDR to overcome the limitations of sensing spatial resolution and sensing accuracy,achieving wavelength super-resolution sensing.Through theoretical analysis,we identified the limitations of traditional cross-correlation demodulation algorithms regarding wavelength and spatial resolution,as well as the positioning errors for correlation peaks.By leveraging the phase-shifting properties,we transformed the peak-finding into a zero-finding problem.Furthermore,we employed fast Fourier transform interpolation and linear interpolation near the zeros to achieve precise localization of the correlation delay.Through theoretical simulations and experiments,the error evolution patterns of the algorithm under uniform strain and sinusoidal strain distributions were investigated,and it was demonstrated that a high signal-to-noise ratio environment helps improve demodulation accuracy.Strain experiments validated a sensing spatial resolution on the order of 350μm and a sensing accuracy of∼3.5με.In addition,shape reconstruction was achieved through standard 2D semicircular shape reconstruction experiments at a spatial resolution of 350μm,with only a 0.33%reconstruction error.We propose a new direction for correlation demodulation schemes,which have promising applications in fields such as medical devices and hyper-continuum robot shape sensing requiring ultra-high spatial resolution. 展开更多
关键词 super-resolution measurement optical frequency domain reflectometer distributed strain sensing phase shift convolution
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Solvatochromic BODIPYs with Polarity-Independent Absorption for Functional Super-resolution Imaging of Live Cell Membranes 认领 引用
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作者 Yunshu Liu Xiayi Gong +4 位作者 Bryn Yoder Jessica M.Gluck Nelson R.Vinueza Xiaojing Liu Yang Zhang 《Chemical & Biomedical Imaging》 CAS CSCD 2026年第4期527-532,共6页
Functional super-resolution microscopy(SRM)has recently emerged as a powerful technique for concurrently mapping nanoscale physicochemical properties of the subcellular environment.This is accomplished by capturing bo... Functional super-resolution microscopy(SRM)has recently emerged as a powerful technique for concurrently mapping nanoscale physicochemical properties of the subcellular environment.This is accomplished by capturing both the spatial localization and single-molecule fluorescence spectral shifts of environmentally sensitive fluorescent probes.However,current fluorophore choices for functional SRM are largely limited to Nile Red derivatives,which suffer from several limitations.Here we report two solvatochromic and far-red-emitting fluorophores with polarityindependent absorption specifically tailored for functional SRM,based on a boron dipyrromethene(BODIPY)chromophore.We systematically characterize their photophysical and solvatochromic properties and directly compare their performance to that of Nile Red.A zwitterionic lipid is introduced to the dye to enable transient-binding-based single-molecule switching and to achieve selective targeting of the plasma membrane in live cells.The functional SRM capability of this probe is demonstrated by mapping nanoscale membrane polarity changes under the influence of cholesterol in live U2OS cells with a localization precision of 16−20 nm. 展开更多
关键词 BODIPY Solvatochromism Functional Super-resolution Microscopy Single-Molecule Spectroscopy Polarity Mapping Membrane Imaging
Seeing the Unseen:Super-Resolution Microscopy in Protein Aggregation Research 认领 引用
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作者 Molly J.M.Turner Junsheng Chen +1 位作者 Nikos S.Hatzakis Min Zhang 《Chemical & Biomedical Imaging》 CAS CSCD 2026年第2期116-129,共14页
Super-resolution microscopy surpasses the diffraction limit and enables the visualization of biomolecular structures with unprecedented detail.These techniques have been widely used in many scientific areas,including ... Super-resolution microscopy surpasses the diffraction limit and enables the visualization of biomolecular structures with unprecedented detail.These techniques have been widely used in many scientific areas,including cell biology,genomics,microbiology,and material science.In the field of protein aggregation,a process intimately linked to numerous neurodegenerative diseases,the high spatial resolution of super-resolution microscopy enables the direct observation of the fine structure of different species,ranging from small oligomers to mature aggregates,providing insights into molecular aggregation mechanisms and the pathology of neurodegenerative diseases,such as Parkinson’s,Alzheimer’s,and Huntington’s disease.In this review,we outline the principles of three major super-resolution microscopy techniques,including stimulated emission depletion(STED),structured illumination microscopy(SIM),and single-molecule localization microscopy(SMLM),and compare their respective strengths and limitations in studying protein aggregation.We then highlight the recent applications of these techniques in studying protein aggregation,with a focus on aggregate morphology,dynamic formation processes,and interactions with cellular components. 展开更多
关键词 super-resolution microscopy protein aggregation neurodegenerative disease morphology aggregation pathways distribution toxicity pathology
Optimizing Small Object Detection in Low-Resolution Imagery:A Unified Super-Resolution and Detection Framework Using YOLO-Flex 认领 引用
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作者 Abdulhamid Victor Ibrahim Haoyuan Li +1 位作者 Bingyang Guo Ruiyun Yu 《Computers, Materials & Continua》 SCIE EI 2026年第10期1048-1080,共33页
The detection of small objects in low-resolution aerial imagery presents a persistent challenge in computer vision,where hardware constraints,imaging altitude,and scene complexity collectively degrade spatial detail t... The detection of small objects in low-resolution aerial imagery presents a persistent challenge in computer vision,where hardware constraints,imaging altitude,and scene complexity collectively degrade spatial detail to the point where standard detection frameworks fail.Existing super-resolution methods offer partial remedies but are limited by substantial computational costs and by feature discrepancies between Generative Adversarial Network-enhanced and real high-resolution images that degrade downstream detection accuracy.This paper presents YOLO-Flex,a unified framework that addresses these challenges through the co-design of a super-resolution module and a task-adapted object detection network,jointly optimized through a shared loss function.The YOLO-Flex approach employs a Generative Adversarial Network(GAN)-based super-resolution module in which the Residual Feature Cluster Network(RFCN)serves as the generator,trained adversarially against the Average Relativistic Discriminator to produce high-quality super-resolved images from low-resolution inputs,preserving block-level residual features that standard super-resolution-based methods progressively integrate in deep networks.The detection network is restructured from the YOLOv5 baseline through principled backbone reduction,addition of a stride-4 detection head,task-aware SE attention placement,and a trimmed bidirectional feature pyramid prioritizing high-resolution fusion pathways.The complete YOLO-Flex framework achieves competitive super-resolution quality with Peak Signal-to-Noise Ratio(PSNR)of 31.852 dB on DOTA,surpassing notable super-resolution techniques,while the detector subnetwork contains 7.05 million parameters and requires 8.0 GFLOPs,representing a 6.0 times reduction in computational cost compared to the YOLOv5m baseline.Experimental validation is conducted on three benchmark datasets:VisDrone2019-DET,DOTA,and COWC. 展开更多
关键词 Image super-resolution small object detection residual feature cluster feature representation YOLO
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Advances in fluorescent nanoprobes for live-cell super-resolution imaging 认领 引用 被引量：2
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作者 Peng Xu Zexuan Dong +2 位作者 Simei Zhong Yu-Hui Zhang Wei Shen 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2025年第3期3-23,共21页
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
关键词 Super-resolution imaging fluorescent nanoprobe live-cell imaging
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Super-resolution microscopy:Shedding new light on blood cell imaging 认领 引用 被引量：2
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作者 Huan Deng Yan Ma Yu-Hui Zhang 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2025年第1期29-53,共25页
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
关键词 Super-resolution imaging blood cells subcellular structure proteins
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Pore structure properties characterization of shale using generative adversarial network:Image augmentation,super-resolution reconstruction,and multi-mineral auto-segmentation 认领 引用 被引量：1
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作者 LIU Fugui YANG Yongfei +7 位作者 YANG Haiyuan TAO Liu TAO Yunwei ZHANG Kai SUN Hai ZHANG Lei ZHONG Junjie YAO Jun 《Petroleum Exploration and Development》 SCIE 2025年第5期1262-1274,共13页
Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive... Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive framework based on generative adversarial network(GAN)for characterizing pore structure properties of shale,which incorporates image augmentation,super-resolution reconstruction,and multi-mineral auto-segmentation.Using real 2D and 3D shale images,the framework was assessed through correlation function,entropy,porosity,pore size distribution,and permeability.The application results show that this framework enables the enhancement of 3D low-resolution digital cores by a scale factor of 8,without paired shale images,effectively reconstructing the unresolved fine-scale pores under a low resolution,rather than merely denoising,deblurring,and edge clarification.The trained GAN-based segmentation model effectively improves manual multi-mineral segmentation results,resulting in a strong resemblance to real samples in terms of pore size distribution and permeability.This framework significantly improves the characterization of complex shale microstructures and can be expanded to other heterogeneous porous media,such as carbonate,coal,and tight sandstone reservoirs. 展开更多
关键词 shale pore structure parameter generative adversarial network super-resolution multi-mineral auto-segmentation multiscale fusion
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Multiparametric magnetic resonance imaging of deep learning-based super-resolution reconstruction for predicting histopathologic grade in hepatocellular carcinoma 认领 引用
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作者 Zi-Zheng Wang Shao-Ming Song +3 位作者 Gong Zhang Rui-Qiu Chen Zhuo-Chao Zhang Rong Liu 《World Journal of Gastroenterology》 SCIE CAS 2025年第34期68-80,共13页
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
关键词 Super-resolution reconstruction Magnetic resonance imaging Hepatocellular carcinoma Histopathologic grade Radiomics
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