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Conference on Satellite Data Compression, Communications, and Processing VI

作者： He, Mingyi Liu, Weihua Bai, Lin Northwestern Polytech Univ Sch Elect & Informat Shaanxi Key Lab Informat Acquisit & Proc Xian 710129 Peoples R China

ISBN: (纸本)9780819483065

This paper proposes a new regularization algorithm combining the wavelet-based and contourlet-based regularization items based on the Compressive Sensing (CS) theorem. The new algorithm aims at gaining maximum benefit by combining the multiscale and multiresolution properties common to both wavelet and contourlet schemes, while simultaneously incorporating their individual properties of point singularity and line singularity respectively. CS is applied to remote sensing image deblurring. It has great practical significance due to saving the hardware cost and aiding fast transmission. Experimental results show the method achieves improvement in peak-signal-noise-ratio and correlation function as compared to traditional regularization algorithms.

关键词： compressive sensing theorem remote sensing image image restoration iterative shrinkage algorithm regularization

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Using LASSO for formulating constraint of least-squares programming for solving one-norm equality constrained problem

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SIGNAL IMAGE AND VIDEO PROCESSING 2017年第1期11卷 179-186页

作者： Li, Ya Ling, Bingo Wing-Kuen Xie, Langxiong Dai, Qingyun Guangdong Univ Technol Fac Informat Engn Guangzhou 510006 Guangdong Peoples R China

The paper proposes an efficient method for solving a one-norm equality constrained optimization problem. In fact, this kind of optimization problems is nonconvex. First, the problem is formulated as the least absolute shrinkage and selection operator (LASSO) optimization problem. Then, it is solved by iterative shrinkage algorithms such as the fast iterative shrinkage thresholding algorithm. Next, the solution of the LASSO optimization problem is employed for formulating the constraint of the corresponding least-squares constrained optimization problem. The solution of the least-squares constrained optimization problem is taken as a near globally optimal solution of the one-norm equality constrained optimization problem. The main advantage of this proposed method is that a solution with both lower one-norm constraint error and two-norm reconstruction error can be obtained compared to those of the LASSO problem, while the required computational power is significantly reduced compared to the full search approach. Computer numerical simulation results are illustrated.

关键词： One-norm equality constrained optimization Least absolute shrinkage and selection operator optimization iterative shrinkage algorithm Fast iterative shrinkage thresholding algorithm Least-squares constrained optimization Nonconvex optimization

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Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm

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JOURNAL OF MAGNETIC RESONANCE 2018年 286卷 91-98页

作者： Elahi, Sana Kaleem, Muhammad Omer, Hammad COMSATS Inst Informat Technol Dept Elect Engn Islamabad Pakistan

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k-space. This paper introduces an improved iterative algorithm based on p-thresholding technique for CS-MRI image reconstruction. The use of p-thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p-thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p-thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors. (C) 2017 Elsevier Inc. All rights reserved.

关键词： MRI Compressed sensing Non-linear reconstruction iterative shrinkage algorithm

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Group sparsity residual constraint for image denoising with external nonlocal self-similarity prior

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NEUROCOMPUTING 2018年 275卷 2294-2306页

作者： Zha, Zhiyuan Zhang, Xinggan Wang, Qiong Bai, Yechao Chen, Yang Tang, Lan Liu, Xin Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Jiangsu Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210023 Jiangsu Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Oulu 90014 Finland

Nonlocal image representation has been successfully used in many image-related inverse problems including denoising, deblurring and deblocking. However, most existing methods only consider the nonlocal self-similarity (NSS) prior of degraded observation image, and few methods use the NSS prior from natural images. In this paper we propose a novel method for image denoising via group sparsity residual constraint with external NSS prior (GSRC-ENSS). Different from the previous NSS prior-based denoising methods, two kinds of NSS prior (e.g., NSS priors of noisy image and natural images) are used for image denoising. In particular, to enhance the performance of image denoising, the group sparsity residual is proposed, and thus the problem of image denoising is translated into reducing the group sparsity residual. Because the groups contain a large amount of NSS information of natural images, to reduce the group sparsity residual, we obtain a good estimation of the group sparse coefficients of the original image by the external NSS prior based on Gaussian Mixture Model (GMM) learning, and the group sparse coefficients of noisy image are used to approximate the estimation. To combine these two NSS priors better, an effective iterative shrinkage algorithm is developed to solve the proposed GSRC-ENSS model. Experimental results demonstrate that the proposed GSRC-ENSS not only outperforms several state-of-the-art methods, but also delivers the best qualitative denoising results with finer details and less ringing artifacts. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Image denoising Group sparsity residual constraint Nonlocal self-similarity Gaussian Mixture Model iterative shrinkage algorithm

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Super-resolution with Nonlocal Regularized Sparse Representation

Super-resolution with Nonlocal Regularized Sparse Representa...

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Conference on Visual Communications and Image Processing (VCIP)

作者： Dong, Weisheng Shi, Guangming Zhang, Lei Wu, Xiaolin Xidian Univ Sch Elect Engn Chinese Minist Educ Key Lab Intelligent Percept & Image Understanding Xian Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada

ISBN: (纸本)9780819482341

The reconstruction of a high resolution (HR) image from its low resolution (LR) counterpart is a challenging problem. The recently developed sparse representation (SR) techniques provide new solutions to this inverse problem by introducing the l(1)-norm sparsity prior into the super-resolution reconstruction process. In this paper, we present a new SR based image super-resolution by optimizing the objective function under an adaptive sparse domain and with the nonlocal regularization of the HR images. The adaptive sparse domain is estimated by applying principal component analysis to the grouped nonlocal similar image patches. The proposed objective function with nonlocal regularization can be efficiently solved by an iterative shrinkage algorithm. The experiments on natural images show that the proposed method can reconstruct HR images with sharp edges from degraded LR images.

关键词： sparse representation nonlocal self-similarity super-resolution iterative shrinkage algorithm

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Accelerating techniques for Sparse Regularization based Tomography Reconstruction

Accelerating techniques for Sparse Regularization based Tomo...

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5th International Conference on Digital Image Processing (ICDIP)

作者： Wang, Liyan Wei, Zhihui Luo, Shouhua Nanjing Univ Sci & Technol Sch Comp Sci & Technol Nanjing 210094 Jiangsu Peoples R China Southeast Univ Dept Math Nanjing 210096 Jiangsu Peoples R China Southeast Univ Sch Biol Sci & Med Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9780819493057

For tomography reconstruction, the iteration methods based on spare regularization have recently emerged and are proven effective especially in the situation that projection data is insufficient or noisy in low radiation dose. Because iterative tomography reconstruction algorithms have heavy computational demanding especially for clinical data sets and is far from being close to real-time reconstruction. So there is incentive to develop fast algorithms of the optimization problem. We present new accelerating iterative shrinkage algorithms for sparse-based tomography reconstruction, which base on the existing shrinkage algorithms and combine with traditional algebra methods, linear search method and preconditioning techniques to solve large dense linear systems. We give two different weighted matrixes as the preconditioner and get the different convergence speeds. From the experimental results it can be seen that using the sparsity in the transform domain as the regularization term can greatly improve the visual effect of the reconstructed images compared with corresponding algebraic algorithms, and the linear search method can obviously accelerates the converge rate.

关键词： tomography reconstruction sparse regularization preconditioned gradient descent algorithm linear search method iterative shrinkage algorithm surrogate function

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GROUP SPARSITY RESIDUAL WITH NON-LOCAL SAMPLES FOR IMAGE DENOISING

GROUP SPARSITY RESIDUAL WITH NON-LOCAL SAMPLES FOR IMAGE DEN...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zha, Zhiyuan Zhang, Xinggan Wang, Qiong Bai, Yechao Tang, Lan Yuan, Xin Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Jiangsu Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210023 Jiangsu Peoples R China Nokia Bell Labs 600 Mt Ave Murray Hill NJ 07974 USA

ISBN: (纸本)9781538646588

Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in GSR is to estimate the residual by using a proper reference of the group-based sparse coding (GSC), which is desired to be as close to the truth as possible. Previous researches utilized the estimations from other algorithms (i.e., GMM or BM3D), which are either not accurate or too slow. In this paper, we propose to use the Non-Local Samples (NLS) as reference in the GSR regime for image denoising, thus termed GSR-NLS. More specifically, we first obtain a good estimation of the group sparse coefficients by the image nonlocal self-similarity, and then solve the GSR model by an effective iterative shrinkage algorithm. Experimental results demonstrate that the proposed GSR-NLS not only outperforms many state-of-the-art methods, but also delivers the competitive advantage of speed.

关键词： Image denoising group-based sparse coding group sparsity residual nonlocal self-similarity iterative shrinkage algorithm

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GROUP SPARSITY RESIDUAL WITH NON-LOCAL SAMPLES FOR IMAGE DENOISING

GROUP SPARSITY RESIDUAL WITH NON-LOCAL SAMPLES FOR IMAGE DEN...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zhiyuan Zha Xinggan Zhang Qiong Wang Yechao Bai Lan Tang Xin Yuan School of Electronic Science and Engineering Nanjing University Nanjing 210023 China Nokia Bell Labs 600 Mountain Avenue Murray Hill NJ 07974 USA

ISBN: (纸本)9781538646595

关键词： Image denoising group-based sparse coding group sparsity residual nonlocal self-similarity iterative shrinkage algorithm image denoising Galvanic Skin Response images RESIDUAL Reference Groups Sparse coding Gm(m) competitive advantage truth Imagery (Psychotherapy)

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