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Shear Let Transform Residual Learning Approach for Single-Image super-resolution

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Computers, Materials & Continua 2024年第5期79卷 3193-3209页

作者： Israa Ismail Ghada Eltaweel Mohamed Meselhy Eltoukhy Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia

super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.super-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance imaging.super-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater clarity.This study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep super-resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution image.The shearlet transform is chosen for its excellent sparse approximation capabilities.Initially,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high frequencies.The shearlet coefficients are fed into the EDSR network.The high-resolution image is subsequently reconstructed using the inverse shearlet transform.The incorporation of the EDSR network enhances training stability,leading to improved generated images.The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image quality.Compared to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

关键词： super-resolution shearlet transform shearlet coefficients enhanced deep super-resolution network

OGSRN:Optical-guided super-resolution network for SAR image

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Chinese Journal of Aeronautics 2022年第5期35卷 204-219页

作者： Yanshan LI Li ZHOU Fan XU Shifu CHEN ATR National Key Laboratory of Defense Technology Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhen 518060China

Although Convolutional Neural Networks(CNNs)have significantly improved the development of image super-resolution(SR)technology in recent years,the existing SR methods for SAR image with large scale factors have rarely been studied due to technical difficulty.A more efficient method is to obtain comprehensive information to guide the SAR image reconstruction.Indeed,the co-registered High-resolution(HR)optical image has been successfully applied to enhance the quality of SAR image due to its discriminative characteristics.Inspired by this,we propose a novel Optical-Guided super-resolution Network(OGSRN)for SAR image with large scale factors.Specifically,our proposed OGSRN consists of two sub-nets:a SAR image superresolution U-Net(SRUN)and a SAR-to-Optical Residual Translation Network(SORTN).The whole process during training includes two stages.In stage-1,the SR SAR images are reconstructed by the SRUN.And an Enhanced Residual Attention Module(ERAM),which is comprised of the Channel Attention(CA)and Spatial Attention(SA)mechanisms,is constructed to boost the representation ability of the network.In stage-2,the output of the stage-1 and its corresponding HR SAR images are translated to optical images by the SORTN,respectively.And then the differences between SR images and HR images are computed in the optical space to obtain feedback information that can reduce the space of possible SR solution.After that,we can use the optimized SRUN to directly produce HR SAR image from Low-resolution(LR)SAR image in the testing phase.The experimental results show that under the guidance of optical image,our OGSRN can achieve excellent performance in both quantitative assessment metrics and visual quality.

关键词： SAR image super-resolution Optical image Attention mechanisms Convolutional Nerual Networks(CNNs)

SR-AFU: super-resolution network using adaptive frequency component upsampling and multi-resolution features

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Frontiers of Computer Science 2023年第1期17卷 123-132页

作者： Ke-Jia CHEN Mingyu WU Yibo ZHANG Zhiwei CHEN School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China College of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China

Image super-resolution (SR) is one of the classic computer vision tasks. This paper proposes a super-resolution network based on adaptive frequency component upsampling, named SR-AFU. The network is composed of multiple cascaded dilated convolution residual blocks (CDCRB) to extract multi-resolution features representing image semantics, and multiple multi-size convolutional upsampling blocks (MCUB) to adaptively upsample different frequency components using CDCRB features. The paper also defines a new loss function based on the discrete wavelet transform, making the reconstructed SR images closer to human perception. Experiments on the benchmark datasets show that SR-AFU has higher peak signal to noise ratio (PSNR), significantly faster training speed and more realistic visual effects compared with the existing methods.

关键词： super-resolution multi-resolution features adaptive frequency upsampling wavelet transformation

super-resolution parameter estimation of monopulse radar by wide-narrowband joint processing

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系统工程与电子技术:英文版 2023年第5期34卷 1158-1170页

作者： CAI Tianyi DAN Bo HUANG Weibo Sichuan Institute of Aerospace Electronic Equipment Chengdu 610100China Coastal Defense College Naval Aviation UniversityYantai 264001China

The angular resolution of radar is of crucial signifi-cance to its tracking performance.In this paper,a super-resolu-tion parameter estimation algorithm based on wide-narrowband joint processing is proposed to improve the angular resolution of wideband monopulse radar.The range cells containing resolv-able scattering points are detected in the wideband mode,and these range cells are adopted to estimate part of the target parameters by algorithms of low computational requirement.Then,the likelihood function of the echo is constructed in the narrow-band mode to estimate the rest of the parameters,and the parameters estimated in the wideband mode are employed to reduce computation and enhance estimation accuracy.Simu-lation results demonstrate that the proposed algorithm has higher estimation accuracy and lower computational complexity than the current algorithm and can avoid the risk of model mis-match.

关键词： monopulse radar super-resolution wide-narrow band processing parameter estimation

super-resolution DOA estimation for correlated off-grid signals via deep estimator

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Journal of Systems Engineering and Electronics 2022年第6期33卷 1096-1107页

作者： WU Shuang YUAN Ye ZHANG Weike YUAN Naichang Facility Design and Instrumentation Institute China Aerodynamics Research and Development CenterMianyang 621000China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System National University of Defense TechnologyChangsha 410073China

This paper develops a deep estimator framework of deep convolution networks(DCNs)for super-resolution direction of arrival(DOA)estimation.In addition to the scenario of correlated signals,the quantization errors of the DCN are the major challenge.In our deep estimator framework,one DCN is used for spectrum estimation with quantization errors,and the remaining two DCNs are used to estimate quantization errors.We propose training our estimator using the spatial sampled covariance matrix directly as our deep estimator’s input without any feature extraction operation.Then,we reconstruct the original spatial spectrum from the spectrum estimate and quantization errors estimate.Also,the feasibility of the proposed deep estimator is analyzed in detail in this paper.Once the deep estimator is appropriately trained,it can recover the correlated signals’spatial spectrum fast and accurately.Simulation results show that our estimator performs well in both resolution and estimation error compared with the state-of-the-art algorithms.

关键词： off-grid direction of arrival(DOA)estimation deep convolution network(DCN) correlated signal quantization error super-resolution

A Review of Clinical Applications for super-resolution Ultrasound Localization Microscopy

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Current Medical Science 2022年第1期42卷 1-16页

作者： Hui-ming YI Matthew RLowerison Peng-fei SONG Wei ZHANG eDpartment of Medical Ultrasound Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Eckman Institute for Advanced Science and Technology University of Illinois at Urbana-ChampaignUrbana 61801USA Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbana 61801USA

Microvascular structure and hemodynamics are important indicators for the diagnosis and assessment of many diseases and pathologies.The structural and functional imaging of tissue microvasculature in vivo is a clinically significant objective for the development of many imaging modalities.Contrast-enhanced ultrasound(CEUS)is a popular clinical tool for characterizing tissue microvasculature,due to the moderate cost,wide accessibility,and absence of ionizing radiation of ultrasound.

关键词： contrast-enhanced ultrasound super-resolution ultrasound microvascular imaging microbubbles

Information Purification Network for Remote Sensing Image super-resolution

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Tsinghua Science and Technology 2023年第2期28卷 310-321页

作者： Zheyuan Wang Liangliang Li Linxin Xing Jiawen Wang Kaipeng Sun Hongbing Ma College of Information Science and Engineering Xinjiang UniversityUrumqi 830046China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Shanghai Institute of Satellite Engineering Shanghai 201109China

Recently,several well-performing deep convolutional neural networks were proposed for remote sensing image super-resolution(SR).However,these methods rarely consider that remote sensing images are corruptible by additional noise,blurring,and other factors.Therefore,to eliminate the interference of these factors,especially the noise,we propose a novel information purification network(IPN)for remote sensing image SR.The proposed information purification block(IPB)can process channel-wise features differently by channel separation and rescale spatial-wise features adaptively through the proposed multi-scale spatial attention mechanism.We further design an information group to explore a more powerful expressive combination of IPBs.Moreover,long and short skip connections can transmit abundant low-frequency information,making IPBs pay more attention to high-frequency information.We mix the images under various degradation models as training data in the training phase.In this way,the network can directly reconstruct various degraded images.Experiments on AID and UC Merced Land-Use datasets under multiple degradation models demonstrate that the proposed IPN performs better than state-of-the-art methods.

关键词： deep convolutional neural networks remote sensing image super-resolution information purification network

IRMIRS:Inception-ResNet-Based Network for MRI Image super-resolution

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1121-1142页

作者： Wazir Muhammad Zuhaibuddin Bhutto Salman Masroor Murtaza Hussain Shaikh Jalal Shah Ayaz Hussain Department of Electrical Engineering Balochistan University of Engineering and TechnologyKhuzdar89100Pakistan Department of Computer Systems Engineering Balochistan University of Engineering and TechnologyKhuzdar89100Pakistan Department of Mechanical Engineering Balochistan University of Engineering and TechnologyKhuzdar89100Pakistan Department of Mechanical Engineering National Taiwan University of Science and Technology10607Taiwan Department of Information Systems Kyungsung UniversityBusan613010South Korea

Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues.These challenges are increasing the interest in the quality of medical images.Recent research has proven that the rapid progress in convolutional neural networks(CNNs)has achieved superior performance in the area of medical image super-resolution.However,the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance(MR)images,adding extra noise in the models and more memory consumption.Furthermore,conventional deep CNN approaches used layers in series-wise connection to create the deeper mode,because this later end layer cannot receive complete information and work as a dead layer.In this paper,we propose Inception-ResNet-based Network for MRI Image super-resolution known as IRMRIS.In our proposed approach,a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling filters.Furthermore,a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input image.Quantitative and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.

关键词： super-resolution magnetic resonance imaging ResNet block inception block convolutional neural network deconvolution layer

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image super-resolution

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Computers, Materials & Continua 2023年第6期75卷 5159-5176页

作者： Kun Yang Lei Zhao Xianghui Wang Mingyang Zhang Linyan Xue Shuang Liu Kun Liu College of Quality and Technical supervision Hebei UniversityBaoding071002China Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System Baoding071002China Postdoctoral Research Station of Optical Engineering Hebei UniversityBaoding071000China

The diagnosis of COVID-19 requires chest computed tomography(CT).High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease,so it is of clinical importance to study super-resolution(SR)algorithms applied to CT images to improve the reso-lution of CT images.However,most of the existing SR algorithms are studied based on natural images,which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth,which is not suitable for machines with limited resources.To alleviate these issues,we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution(RFAFN).Specifically,we design a contextual feature extraction block(CFEB)that can extract CT image features more efficiently and accurately than ordinary residual blocks.In addition,we propose a feature-weighted cascading strategy(FWCS)based on attentional feature fusion blocks(AFFB)to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information.Finally,we suggest a global hierarchical feature fusion strategy(GHFFS),which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels.Numerous experiments show that our method performs better than most of the state-of-the-art(SOTA)methods on the COVID-19 chest CT dataset.In detail,the peak signal-to-noise ratio(PSNR)is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at×3 SR compared to the suboptimal method,but the number of parameters and multi-adds are reduced by 22K and 0.43G,respectively.Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.

关键词： super-resolution COVID-19 chest CT lightweight network contextual feature extraction attentional feature fusion