期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到77篇文章
< 1 2 … 4 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
AESR3D:3D overcomplete autoencoder for trabecular computed tomography super resolution 认领 引用
1
作者 Shuwei Zhang Yefeng Liang +3 位作者 Xingyu Li Shibo Li Xiaofeng Xiong Lihai Zhang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第3期652-665,共14页
Osteoporosis is a major cause of bone fracture and can be characterised by both mass loss and microstructure deterioration of the bone.The modern way of osteoporosis assessment is through the measurement of bone miner... Osteoporosis is a major cause of bone fracture and can be characterised by both mass loss and microstructure deterioration of the bone.The modern way of osteoporosis assessment is through the measurement of bone mineral density,which is not able to unveil the pathological condition from the mesoscale aspect.To obtain mesoscale information from computed tomography(CT),the super-resolution(SR)approach for volumetric imaging data is required.A deep learning model AESR3D is proposed to recover high-resolution(HR)Micro-CT from low-resolution Micro-CT and implement an unsupervised segmentation for better trabecular observation and measurement.A new regularisation overcomplete autoencoder framework for the SR task is proposed and theoretically analysed.The best performance is achieved on structural similarity measure of trabecular CT SR task compared with the state-of-the-art models in both natural and medical image SR tasks.The HR and SR images show a high correlation(r=0.996,intraclass correlation coefficients=0.917)on trabecular bone morphological indicators.The results also prove the effectiveness of our regularisation framework when training a large capacity model. 展开更多
关键词 overcomplete autoencoder segmentation super resolution trabecular CT
暂未订购 下载PDF
Improved Network for Face Recognition Based on Feature Super Resolution Method 认领 引用 被引量：3
2
作者 Ling-Yi Xu Zoran Gajic 《International Journal of Automation and computing》 CSCD 2021年第6期915-925,共11页
Low-resolution face images can be found in many practical applications. For example, faces captured from surveillance videos are typically in small sizes. Existing face recognition deep networks, trained on high-resol... Low-resolution face images can be found in many practical applications. For example, faces captured from surveillance videos are typically in small sizes. Existing face recognition deep networks, trained on high-resolution images, perform poorly in recognizing low-resolution faces. In this work, an improved multi-branch network is proposed by combining ResNet and feature super-resolution modules. ResNet is for recognizing high-resolution facial images and extracting features from both high-and low-resolution images.Feature super-resolution modules are inserted before the classifier of ResNet for low-resolution facial images. They are used to increase feature resolution. The proposed method is effective and simple. Experimental results show that the recognition accuracy for high-resolution face images is high, and the recognition accuracy for low-resolution face images is improved. 展开更多
关键词 Face recognition feature super resolution multiple-branch network deep learning convolutional neural networks
暂未订购 下载PDF
A Regularized Super Resolution Algorithm for Generalized Gaussian Noise 认领 引用 被引量：1
3
作者 陈文 方向忠 +3 位作者 刘立峰 蒋伟 丁大为 乔艳涛 《Journal of Donghua University(English Edition)》 EI CAS 2010年第1期25-35,共11页
In this paper,an iterative regularized super resolution (SR) algorithm considering non-Gaussian noise is proposed.Based on the assumption of a generalized Gaussian distribution for the contaminating noise,an lp norm i... In this paper,an iterative regularized super resolution (SR) algorithm considering non-Gaussian noise is proposed.Based on the assumption of a generalized Gaussian distribution for the contaminating noise,an lp norm is adopted to measure the data fidelity term in the cost function.In the meantime,a regularization functional defined in terms of the desired high resolution (HR) image is employed,which allows for the simultaneous determination of its value and the partly reconstructed image at each iteration step.The convergence is thoroughly studied.Simulation results show the effectiveness of the proposed algorithm as well as its superiority to conventional SR methods. 展开更多
关键词 super resolution generalized p-Gaussian distribution regularization parameter
暂未订购 下载PDF
OBJECT-BASED SUPER RESOLUTION FOR INTELLIGENT VISUAL SURVEILLANCE VIDEO 认领 引用 被引量：1
4
作者 Wang Suyu Shen Lansun 《Journal of Electronics(China)》 2008年第1期140-144,共5页
Construction of high resolution images from low resolution sequences is often im- portant in surveillance applications. In this letter, an affine based multi-scale block-matching image registration algorithm is first ... Construction of high resolution images from low resolution sequences is often im- portant in surveillance applications. In this letter, an affine based multi-scale block-matching image registration algorithm is first proposed. The images to be registered are divided into overlapped blocks of different size according to its motions. The Least Square (LS) image reg- istration algorithm is extended to match the blocks. Then an object based Super Resolution (SR) scheme is designed, the Maximum A Priori (MAP) super resolution algorithm is extended to enhance the resolution of the interest objects. Experimental results show that the proposed multi-scale registration method provides more accurate registration between frames. Further more, the object based super resolution scheme shows an enhanced performance compared with the traditional MAP method. 展开更多
关键词 Super Resolution （SR） reconstruction Visual surveillance Maximum A Priori （MAP） Affine model Image registration
暂未订购 下载PDF
A Unified Model Fusing Region of Interest Detection and Super Resolution for Video Compression 认领 引用
5
作者 Xinkun Tang Feng Ouyang +2 位作者 Ying Xu Ligu Zhu Bo Peng 《Computers, Materials & Continua》 SCIE EI 2024年第6期3955-3975,共21页
High-resolution video transmission requires a substantial amount of bandwidth.In this paper,we present a novel video processing methodology that innovatively integrates region of interest(ROI)identification and super-... High-resolution video transmission requires a substantial amount of bandwidth.In this paper,we present a novel video processing methodology that innovatively integrates region of interest(ROI)identification and super-resolution enhancement.Our method commences with the accurate detection of ROIs within video sequences,followed by the application of advanced super-resolution techniques to these areas,thereby preserving visual quality while economizing on data transmission.To validate and benchmark our approach,we have curated a new gaming dataset tailored to evaluate the effectiveness of ROI-based super-resolution in practical applications.The proposed model architecture leverages the transformer network framework,guided by a carefully designed multi-task loss function,which facilitates concurrent learning and execution of both ROI identification and resolution enhancement tasks.This unified deep learning model exhibits remarkable performance in achieving super-resolution on our custom dataset.The implications of this research extend to optimizing low-bitrate video streaming scenarios.By selectively enhancing the resolution of critical regions in videos,our solution enables high-quality video delivery under constrained bandwidth conditions.Empirical results demonstrate a 15%reduction in transmission bandwidth compared to traditional super-resolution based compression methods,without any perceivable decline in visual quality.This work thus contributes to the advancement of video compression and enhancement technologies,offering an effective strategy for improving digital media delivery efficiency and user experience,especially in bandwidth-limited environments.The innovative integration of ROI identification and super-resolution presents promising avenues for future research and development in adaptive and intelligent video communication systems. 展开更多
关键词 Super resolution region of interest detection video compression
暂未订购 下载PDF
Super resolution reconstruction of moving objects from low resolution surveillance video 认领 引用
6
作者 王素玉 Shen Lansun +1 位作者 David Daganfeng Li Xiaoguang 《High Technology Letters》 EI CAS 2008年第2期123-128,共6页
Construction of high resolution images from low resolution sequences having rigid or semi-rigid ob-jects with unified motions is often important in surveillance and other applications.In this paper a novelobject-based... Construction of high resolution images from low resolution sequences having rigid or semi-rigid ob-jects with unified motions is often important in surveillance and other applications.In this paper a novelobject-based super resolution reconstruction scheme was proposed,in which a six-parameter affine model-based object tracking and registration method was first used to segment and match objects among a se-quence of low resolution frames.The motion model was then further extended to the traditional maximuma posterior(MAP)super resolution algorithm.The proposed object tracking and registration method wasevaluated by both simulated and real acquired sequences.The results have demonstrated the high accura-cy of the proposed object based method and the enhanced reconstruction performance of the extended ap-proach. 展开更多
关键词 super resolution reconstruction visual surveillance maximum a posterior （MAP） atone model motion estimation
暂未订购 下载PDF
A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution 认领 引用
7
作者 Wen CHEN Xiang-zhong FANG Yan CHENG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2009年第12期1705-1713,共9页
This paper presents a threshold-free maximum a posteriori (MAP) super resolution (SR) algorithm to reconstruct high resolution (HR) images with sharp edges. The joint distribution of directional edge images is modeled... This paper presents a threshold-free maximum a posteriori (MAP) super resolution (SR) algorithm to reconstruct high resolution (HR) images with sharp edges. The joint distribution of directional edge images is modeled as a multidimensional Lorentzian (MDL) function and regarded as a new image prior. This model makes full use of gradient information to restrict the solution space and yields an edge-preserving SR algorithm. The Lorentzian parameters in the cost function are replaced with a tunable variable, and graduated nonconvexity (GNC) optimization is used to guarantee that the proposed multidimensional Lorentzian SR (MDLSR) algorithm converges to the global minimum. Simulation results show the effectiveness of the MDLSR algorithm as well as its superiority over conventional SR methods. 展开更多
关键词 Edge preservation Multidimensional Lorentzian distribution （MDL） Super resolution Threshold
暂未订购 下载PDF
Multiframe Blind Super Resolution Imaging Based on Blind Deconvolution 认领 引用
8
作者 元伟 张立毅 《Transactions of Tianjin University》 EI CAS 2016年第4期358-366,共9页
As an ill-posed problem, multiframe blind super resolution imaging recovers a high resolution image from a group of low resolution images with some degradations when the information of blur kernel is limited. Note tha... As an ill-posed problem, multiframe blind super resolution imaging recovers a high resolution image from a group of low resolution images with some degradations when the information of blur kernel is limited. Note that the quality of the recovered image is influenced more by the accuracy of blur estimation than an advanced regularization. We study the traditional model of the multiframe super resolution and modify it for blind deblurring. Based on the analysis, we proposed two algorithms. The first one is based on the total variation blind deconvolution algorithm and formulated as a functional for optimization with the regularization of blur. Based on the alternating minimization and the gradient descent algorithm, the high resolution image and the unknown blur kernel are estimated iteratively. By using the median shift and add operator, the second algorithm is more robust to the outlier influence. The MSAA initialization simplifies the interpolation process to reconstruct the blurred high resolution image for blind deblurring and improves the accuracy of blind super resolution imaging. The experimental results demonstrate the superiority and accuracy of our novel algorithms. 展开更多
关键词 blind deconvolution multiframe blind super resolution imaging regularization iteration deblurring
暂未订购 下载PDF
Arbitrary Scale Super Resolution Network for Satellite Imagery 认领 引用 被引量：5
9
作者 Jing Fang Jing Xiao +2 位作者 Xu Wang Dan Chen Ruimin Hu 《China Communications》 SCIE CSCD 2022年第8期234-246,共13页
Recently,satellite imagery has been widely applied in many areas.However,due to the limitations of hardware equipment and transmission bandwidth,the images received on the ground have low resolution and weak texture.I... Recently,satellite imagery has been widely applied in many areas.However,due to the limitations of hardware equipment and transmission bandwidth,the images received on the ground have low resolution and weak texture.In addition,since ground terminals have various resolutions and real-time playing requirements,it is essential to achieve arbitrary scale super-resolution(SR)of satellite images.In this paper,we propose an arbitrary scale SR network for satellite image reconstruction.First,we propose an arbitrary upscale module for satellite imagery that can map low-resolution satellite image features to arbitrary scale enlarged SR outputs.Second,we design an edge reinforcement module to enhance the highfrequency details in satellite images through a twobranch network.Finally,extensive upsample experiments on WHU-RS19 and NWPU-RESISC45 datasets and subsequent image segmentation experiments both show the superiority of our method over the counterparts. 展开更多
关键词 satellite imagery super resolution arbitrary upscale edge reinforcement video satellite
暂未订购 下载PDF
Super Resolution Perception for Improving Data Completeness in Smart Grid State Estimation 认领 引用 被引量：3
10
作者 Gaoqi Liang Guolong Liu +4 位作者 Junhua Zhao Yanli Liu Jinjin Gu Guangzhong Sun Zhaoyang Dong 《Engineering》 SCIE EI CAS 2020年第7期789-800,共12页
The smart grid is an evolving critical infrastructure,which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply services.To cope ... The smart grid is an evolving critical infrastructure,which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply services.To cope with the intermittency of ever-increasing renewable energy and ensure the security of the smart grid,state estimation,which serves as a basic tool for understanding the true states of a smart grid,should be performed with high frequency.More complete system state data are needed to support high-frequency state estimation.The data completeness problem for smart grid state estimation is therefore studied in this paper.The problem of improving data completeness by recovering highfrequency data from low-frequency data is formulated as a super resolution perception(SRP)problem in this paper.A novel machine-learning-based SRP approach is thereafter proposed.The proposed method,namely the Super Resolution Perception Net for State Estimation(SRPNSE),consists of three steps:feature extraction,information completion,and data reconstruction.Case studies have demonstrated the effectiveness and value of the proposed SRPNSE approach in recovering high-frequency data from low-frequency data for the state estimation. 展开更多
关键词 State estimation Low-frequency data High-frequency data Super resolution perception Data completeness
暂未订购 下载PDF
A Novel AlphaSRGAN for Underwater Image Super Resolution 认领 引用 被引量：3
11
作者 Aswathy K.Cherian E.Poovammal 《Computers, Materials & Continua》 SCIE EI 2021年第11期1537-1552,共16页
Obtaining clear images of underwater scenes with descriptive details is an arduous task.Conventional imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition er... Obtaining clear images of underwater scenes with descriptive details is an arduous task.Conventional imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition errors.Consequently,a need for a system that produces clear images for underwater image study has been necessitated.To overcome problems in resolution and to make better use of the Super-Resolution(SR)method,this paper introduces a novel method that has been derived from the Alpha Generative Adversarial Network(AlphaGAN)model,named Alpha Super Resolution Generative Adversarial Network(AlphaSRGAN).The model put forth in this paper helps in enhancing the quality of underwater imagery and yields images with greater resolution and more concise details.Images undergo pre-processing before they are fed into a generator network that optimizes and reforms the structure of the network while enhancing the stability of the network that acts as the generator.After the images are processed by the generator network,they are passed through an adversarial method for training models.The dataset used in this paper to learn Single Image Super Resolution(SISR)is the USR 248 dataset.Training supervision is performed by an unprejudiced function that simultaneously scrutinizes and improves the image quality.Appraisal of images is done with reference to factors like local style information,global content and color.The dataset USR 248 which has a huge collection of images has been used for the study is composed of three collections of images—high(640×480)and low(80×60,160×120,and 320×240).Paired instances of different sizes—2×,4×and 8×—are also present in the dataset.Parameters like Mean Opinion Score(MOS),Peak Signal-to-Noise Ratio(PSNR),Structural Similarity(SSIM)and Underwater Image Quality Measure(UIQM)scores have been compared to validate the improved efficiency of our model when compared to existing works. 展开更多
关键词 Underwater imagery single image super-resolution perceptual quality generative adversarial network image super resolution
暂未订购 下载PDF
Super Resolution Sensing Technique for Distributed Resource Monitoring on Edge Clouds 认领 引用 被引量：1
12
作者 YANG Han CHEN Xu ZHOU Zhi 《ZTE Communications》 2021年第3期73-80,共8页
With the vigorous development of mobile networks,the number of devices at the network edge is growing rapidly and the massive amount of data generated by the devices brings a huge challenge of response latency and com... With the vigorous development of mobile networks,the number of devices at the network edge is growing rapidly and the massive amount of data generated by the devices brings a huge challenge of response latency and communication burden.Existing resource monitoring systems are widely deployed in cloud data centers,but it is difficult for traditional resource monitoring solutions to handle the massive data generated by thousands of edge devices.To address these challenges,we propose a super resolution sensing(SRS)method for distributed resource monitoring,which can be used to recover reliable and accurate high‑frequency data from low‑frequency sampled resource monitoring data.Experiments based on the proposed SRS model are also conducted and the experimental results show that it can effectively reduce the errors generated when recovering low‑frequency monitoring data to high‑frequency data,and verify the effectiveness and practical value of applying SRS method for resource monitoring on edge clouds. 展开更多
关键词 edge clouds super resolution sensing distributed resource monitoring
暂未订购 下载PDF
Deep Learned Singular Residual Network for Super Resolution Reconstruction 认领 引用
13
作者 Gunnam Suryanarayana D.Bhavana +2 位作者 P.E.S.N.Krishna Prasad M.M.K.Narasimha Reddy Md Zia Ur Rahman 《Computers, Materials & Continua》 SCIE EI 2023年第1期1123-1137,共15页
Single image super resolution(SISR)techniques produce images of high resolution(HR)as output from input images of low resolution(LR).Motivated by the effectiveness of deep learning methods,we provide a framework based... Single image super resolution(SISR)techniques produce images of high resolution(HR)as output from input images of low resolution(LR).Motivated by the effectiveness of deep learning methods,we provide a framework based on deep learning to achieve super resolution(SR)by utilizing deep singular-residual neural network(DSRNN)in training phase.Residuals are obtained from the difference between HR and LR images to generate LR-residual example pairs.Singular value decomposition(SVD)is applied to each LR-residual image pair to decompose into subbands of low and high frequency components.Later,DSRNN is trained on these subbands through input and output channels by optimizing the weights and biases of the network.With fewer layers in DSRNN,the influence of exploding gradients is reduced.This speeds up the learning process and also improves accuracy by using skip connections.The trained DSRNN parameters yield residuals to recover the HR subbands in the testing phase.Experimental analysis shows that the proposed method results in superior performance to existingmethods in terms of subjective quality.Extensive testing results on popular benchmark datasets such as set5,set14,and urban100 for a scaling factor of 4 show the effectiveness of the proposed method across different qualitative evaluation metrics. 展开更多
关键词 Deep learning image reconstruction residual network singular values super resolution
暂未订购 下载PDF
MTF Measurement of EBCCD Imaging System by Using Super Resolution Technique 认领 引用
14
作者 左昉 高岳 +2 位作者 高稚允 苏美开 周立伟 《Journal of Beijing Institute of Technology》 EI CAS 2003年第2期125-128,共4页
Existing methods of measurement MTF for discrete imaging system are analysed. A slit target is frequently used to measure the MTF for an imaging system. Usually there are four methods to measure the MTF for a discrete... Existing methods of measurement MTF for discrete imaging system are analysed. A slit target is frequently used to measure the MTF for an imaging system. Usually there are four methods to measure the MTF for a discrete imaging system by using a slit. These methods have something imperfect respectively. But for the discrete imaging systems of under sampling it is difficult to reproduce this type of target properly since frequencies above Nyquist are folded into those below Nyquist, resulting in aliasing effect. To tackle the aliasing problem, a super resolution technique is introduced into our measurement, which gives MTF values both above and below Nyquist more accurately. 展开更多
关键词 EBCCD modulation transfer function super resolution low light level imaging
暂未订购 下载PDF
3D nanoscale fabrication and imaging:a multimodal approach for in situ and super-resolution characterization 认领 引用
15
作者 Qiulan Liu Jisen Wen +11 位作者 Liang Xu Zhenyao Yang Chun Cao Shangting You Gangyao Zhan Yiwei Qiu Wenjie Liu Xiaobing Wang Cuifang Kuang Dazhao Zhu Shih-Chi Chen Xu Liu 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第2期639-652,共14页
The synergistic innovation of nanoscale optical fabrication and characterization technologies holds the key to overcoming three-dimensional(3D)precision manufacturing bottlenecks.This study reports the novel self-repo... The synergistic innovation of nanoscale optical fabrication and characterization technologies holds the key to overcoming three-dimensional(3D)precision manufacturing bottlenecks.This study reports the novel self-reporting functionality of 7-diethylamino-3-thenoylcoumarin(DETC)in photoresist,which serves as both a super-resolution photoinitiator and an intrinsic fluorophore with stimulated emission depletion(STED)behavior and polymerization-dependent lifetime characteristics.Through the development of an integrated system combining STED-inspired periphery photoinhibition(PPI)printing with dual-mode imaging,we achieve simultaneous in situ characterization and super-resolution quality verification.Specifically,PPI imaging demonstrates 50-nm lateral resolution for 40-nm printed lines and resolves 200-nm axial gaps when characterizing developed structures.Furthermore,in situ fluorescence lifetime imaging(FLIM)achieves nanometer-level resolution,comparable to confocal microscopy,by utilizing DETC’s lifetime shift to characterize undeveloped structures.This synergy imaging approach resolves the trade-off between resolution and non-destructive detection,while establishing a new paradigm for closed-loop optimization of complex 3D nanodevices,with profound implications for nanophotonics,precision biosensing,and ultrahigh-density optical storage. 展开更多
关键词 super resolution laser direct writing microscopy peripheral photoinhibition in situ imaging fluorescence lifetime imaging
暂未订购 下载PDF
Wavevector resonance modulation in multilayered nanostructures for sub-λ/10 label-free super-resolution imaging 认领 引用
16
作者 Haonan Zhang Xiaoyu Yang +6 位作者 Mingwei Tang Conghui Gan Feihong Lin Emiliano Descrovi Tao Li Xu Liu Qing Yang 《Advanced Photonics Nexus》 CSCD 2026年第4期80-89,共10页
Label-free super-resolution imaging based on the spatial-frequency-shift(SFS)effect enables conventional microscopes to surpass the diffraction limit,holding significant promise for nanoscale inspection in materials s... Label-free super-resolution imaging based on the spatial-frequency-shift(SFS)effect enables conventional microscopes to surpass the diffraction limit,holding significant promise for nanoscale inspection in materials science and biology.However,current SFS approaches generally face a practical trade-off in obtaining both the ultra-high resolution and the high signal-to-noise ratio(SNR)when using high lateral wavevector(kx)illumination supported by a natural waveguide or single metal film.Here,we proposed a wavevector resonance modulation scheme in multilayered nanostructures that introduces an enhanced deep SFS effect,which is realized by surface plasmon polariton illumination supported by a designed multilayer with relaxed fabrication tolerance.This approach simultaneously achieves a peak-topeak distance of∼70 nm under the excitation wavelength of 780 nm,a resonance-enhanced illumination field,and a more than 10-fold expansion for the coherent transfer function(CTF).We demonstrated the simple excitation process by means of gratings and presented an application in nano-imaging experiments for label-free particles.Results show the advance in the resonance-enhanced illumination modulation method for super-resolution imaging,enabling conventional microscopes to detect and distinguish label-free nanoscale structures with characteristic lateral scales down to sub-λ∕10. 展开更多
关键词 wavevector modulation resonance enhancement deep spatial-frequency-shift super resolution imaging label-free imaging
暂未订购 下载PDF
Video Super-Resolution via Effective Spatio-Temporal Alignment Network 认领 引用
17
作者 Bin Guo Xin Wang +5 位作者 Hao Wen Yuhong Fu Jinxing Li Hui Ma Haoqian Wang Yong Xu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期726-738,共13页
Extracting spatio-temporal cues from neighbouring frames is challenging in video super-resolution(VSR).Although deformable alignment-based VSR methods have shown promise in aligning neighbouring frames with the refere... Extracting spatio-temporal cues from neighbouring frames is challenging in video super-resolution(VSR).Although deformable alignment-based VSR methods have shown promise in aligning neighbouring frames with the reference frame,most existing methods rely on one or a few traditional convolutions to estimate motion offsets for spatio-temporal alignment,restricting receptive field size and alignment accuracy.To address these limitations,we propose an effective spatio-temporal alignment network(ESTA-Net)for VSR.The core component of our method is the group convolution-based alignment module(GCBAM),which utilises cascaded group convolutions to learn offsets across both the original and downsampled resolutions.By employing group convolutions rather than traditional convolutions,GCBAM enables the deformable alignment to achieve a wider receptive field with lower computational cost,thereby improving the accuracy of offset estimation.Additionally,the bi-scale alignment strategy within GCBAM enhances robustness to complex and large-scale motions.Furthermore,we introduce an attention-based feature enhancement module(AFEM)to refine the aligned features,focusing on critical details to improve reconstruction quality.Extensive experiments on standard benchmarks show that our ESTA-Net achieves superior VSR performance against other advanced methods,while maintaining a good equilibrium between model size and performance. 展开更多
关键词 group convolution motion offsets spatio‐temporal alignment video super‐resolution
暂未订购 下载PDF
Physically-consistent full-scale unsupervised reconstruction in turbulent flow dynamics 认领 引用 被引量：1
18
作者 Ye Liu Yuntian Chen +2 位作者 Yunpeng Wang Jianchun Wang Shiyi Chen 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期96-115,共20页
An advanced super-resolution reconstruction method for turbulent flows should be efficient,avoiding reliance on high-resolution data;reliable,strictly adhering to physical laws;and accurate,effectively reconstructing ... An advanced super-resolution reconstruction method for turbulent flows should be efficient,avoiding reliance on high-resolution data;reliable,strictly adhering to physical laws;and accurate,effectively reconstructing both small-and large-scale flow structures.To achieve these objectives,we developed TurbRec,an unsupervised,physically-consistent full-scale turbulent reconstruction model.This novel framework is designed to capture both high-and low-frequency information for precise recon-struction of small-and large-scale turbulent structures.TurbRec relies entirely on low-resolution inputs and governing equations,eliminating the need for high-resolution data and ensuring both efficiency and physical fidelity.Inspired by the multi-scale nature of turbulence and the energy cascade process,our approach integrates frequency-aware Fourier feature embeddings,capturing the full spectrum of turbulent frequencies and enhancing the model’s ability to represent intricate spectral details.To maintain physi-cal consistency,we embed the governing equations within the super-resolution process and introduce a series of physics-motivated strategies,including segmented learning with overlapping windows,multi-scale normalization,and an enhanced residual learning network architecture.These innovations address known challenges in turbulent flow reconstruction,such as accurately predicting small-scale structures and ensuring smooth transitions across temporal boundaries.Numerical experiments demonstrate that Tur-bRec consistently outperforms traditional physics-informed neural networks in predicting instantaneous spatial flow structures,turbulent kinetic energy spectra,and various turbulent statistics. 展开更多
关键词 Full scale reconstruction Turbulent flow Unsupervised learning Physics informed neural networks Super resolution
暂未订购 下载PDF
Multiphoton intravital microscopy in small animals of long-term mitochondrial dynamics based on super-resolution radial fluctuations 认领 引用
19
作者 Saeed Bohlooli Darian Jeongmin Oh +8 位作者 Bjorn Paulson Minju Cho Globinna Kim Eunyoung Tak Inki Kim Chan-Gi Pack Jung-Man Namgoong In-Jeoung Baek Jun Ki Kim 《Opto-Electronic Advances》 SCIE EI CAS CSCD 2025年第7期6-21,共16页
We developed an imaging technique combining two-photon computed super-resolution microscopy and suction-based stabilization to achieve the resolution of the single-cell level and organelles in vivo.To accomplish this,... We developed an imaging technique combining two-photon computed super-resolution microscopy and suction-based stabilization to achieve the resolution of the single-cell level and organelles in vivo.To accomplish this,a conventional two-photon microscope was equipped with a 3D-printed holders,which stabilize the tissue surface within the focal plane of immersion objectives.Further computational image stabilization and noise reduction were applied,followed by superresolution radial fluctuations(SRRF)analysis,doubling image resolution,and enhancing signal-to-noise ratios for in vivo subcellular process investigation.Stabilization of<1μm was obtained by suction,and<25 nm were achieved by subsequent algorithmic image stabilization.A Mito-Dendra2 mouse model,expressing green fluorescent protein(GFP)in mitochondria,demonstrated the potential of long-term intravital subcellular imaging.In vivo mitochondrial fission and fusion,mitochondrial status migration,and the effects of alcohol consumption(modeled as an alcoholic liver disease)and berberine treatment on hepatocyte mitochondrial dynamics are directly observed intravitally.Suction-based stabilization in two-photon intravital imaging,coupled with computational super-resolution holds promise for advancing in vivo subcellular imaging studies. 展开更多
关键词 SRRF in vivo subcellular imaging mitochondiral dynamics multiphoton intravital microscopy super resolution radial fluctuations
暂未订购 下载PDF
Super-resolution fluorescence polarization microscopy 认领 引用 被引量：7
20
作者 Karl Zhanghao Juntao Gao +2 位作者 Dayong Jin Xuedian Zhang Peng Xi 《Journal of Innovative Optical Health Sciences》 SCIE EI 2018年第1期1-12,共12页
Fluorescence polarization is related to the dipole orientation of chromophores,making fuores-cence polarization microscopy possible to_reveal structures and functions of tagged cellularorganelles and biological macrom... Fluorescence polarization is related to the dipole orientation of chromophores,making fuores-cence polarization microscopy possible to_reveal structures and functions of tagged cellularorganelles and biological macromolecules.Several recent super resolution techniques have beenapplied to fluorescence polarization microscopy,achieving dipole measurement at nanoscale.In this review,we summarize both difraction limited and super resolution fluorescence polari-zation microscopy techniques,as well as their applications in biological imaging. 展开更多
关键词 Fluorescence polarization microscopy super resolution fluorescence anisotropy linear dichroism polarization modulation
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 4 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈