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Transformer and GAN-Based super-resolution Reconstruction Network for Medical Images

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Tsinghua Science and Technology 2024年第1期29卷 197-206页

作者： Weizhi Du Shihao Tian Arts&Sciences College Washington University in St.LouisSt.LouisMO 63130USA Department of Electric and Computing Engineering Cornell UniversityIthacaNY 14850USA

super-resolution reconstruction in medical imaging has become more demanding due to the necessity of obtaining high-quality images with minimal radiation dose,such as in low-field magnetic resonance imaging(MRI).However,image super-resolution reconstruction remains a difficult task because of the complexity and high textual requirements for diagnosis *** this paper,we offer a deep learning based strategy for reconstructing medical images from low resolutions utilizing Transformer and generative adversarial networks(T-GANs).The integrated system can extract more precise texture information and focus more on important locations through global image matching after successfully inserting Transformer into the generative adversarial network for picture ***,we weighted the combination of content loss,adversarial loss,and adversarial feature loss as the final multi-task loss function during the training of our proposed model *** comparison to established measures like peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM),our suggested T-GAN achieves optimal performance and recovers more texture features in super-resolution reconstruction of MRI scanned images of the knees and belly.

关键词： super-resolution image reconstruction Transformer generative adversarial network(GAN)

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 ***-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance ***-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater *** 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 *** shearlet transform is chosen for its excellent sparse approximation ***,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high *** shearlet coefficients are fed into the EDSR *** high-resolution image is subsequently reconstructed using the inverse shearlet *** incorporation of the EDSR network enhances training stability,leading to improved generated *** 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 *** 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

Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar

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Geodesy and Geodynamics 2024年第5期15卷 504-515页

作者： Xuchong Yi Shuangxi Zhang Yuxuan Zhou School of Geodesy and Geomatics Wuhan UniversityWuhan 430079China Key Laboratory of Geospace Environment and Geodesy Ministry of EducationWuhan 430079China

Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental ***,existing algorithms based on discrete Fourier transform(DFT),multiple signal classification(MUSIC)and compressed sensing,etc.,cannot achieve both low complexity and high resolution *** paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW ***,we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching ***,we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data,which improves the efficiency of continuous target *** two approaches have improved the algorithm efficiency by nearly 2-4 orders of magnitude without losing accuracy and *** experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.

关键词： 2D-MUSIC FMCW radar Moving target tracking super-resolution Algorithm optimization

AFBNet: A Lightweight Adaptive Feature Fusion Module for super-resolution Algorithms

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2315-2347页

作者： Lirong Yin Lei Wang Siyu Lu Ruiyang Wang Haitao Ren Ahmed AlSanad Salman A.AlQahtani Zhengtong Yin Xiaolu Li Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resource and Environment Engineering Guizhou UniversityGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China

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 *** 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 *** module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating *** with dynamic convolution with scale information,the network can extract more differentiated feature *** introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature *** convolution and pixel-based gating mechanisms enhance the module’s ***,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB *** results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.

关键词： super-resolution feature extraction dynamic convolution attention mechanism gate control

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 *** 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 *** 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 ***,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 ***-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)*** addition to the scenario of correlated signals,the quantization errors of the DCN are the major *** our deep estimator framework,one DCN is used for spectrum estimation with quantization errors,and the remaining two DCNs are used to estimate quantization *** propose training our estimator using the spatial sampled covariance matrix directly as our deep estimator’s input without any feature extraction ***,we reconstruct the original spatial spectrum from the spectrum estimate and quantization errors ***,the feasibility of the proposed deep estimator is analyzed in detail in this *** the deep estimator is appropriately trained,it can recover the correlated signals’spatial spectrum fast and *** 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

super-resolution Based on Curvelet Transform and Sparse Representation

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Computer Systems Science & Engineering 2023年第4期45卷 167-181页

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

super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s).In this paper,we proposed a single image super-resolution *** uses the nonlocal mean filter as a prior step to produce a denoised *** proposed algorithm is based on curvelet *** converts the denoised image into low and high frequencies(sub-bands).Then we applied a multi-dimensional interpolation called Lancozos interpolation over both *** parallel,we applied sparse representation with over complete dictionary for the denoised *** proposed algorithm then combines the dictionary learning in the sparse representation and the interpolated sub-bands using inverse curvelet transform to have an image with a higher *** experimental results of the proposed super-resolution algorithm show superior performance and obviously better-recovering images with enhanced *** comparison study shows that the proposed super-resolution algorithm outperforms the *** mean absolute error is 0.021±0.008 and the structural similarity index measure is 0.89±0.08.

关键词： super-resolution Curvelet transform non-local means filter lancozos interpolation sparse representation

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 ***,the co-registered High-resolution(HR)optical image has been successfully applied to enhance the quality of SAR image due to its discriminative *** by this,we propose a novel Optical-Guided super-resolution Network(OGSRN)for SAR image with large scale ***,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 *** stage-1,the SR SAR images are reconstructed by the *** 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 *** stage-2,the output of the stage-1 and its corresponding HR SAR images are translated to optical images by the SORTN,*** 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 *** that,we can use the optimized SRUN to directly produce HR SAR image from Low-resolution(LR)SAR image in the testing *** 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