Image Restoration via Group l2,1 Norm-Based Structural Sparse Representation

2017 ◽  
Vol 32 (04) ◽  
pp. 1854008 ◽  
Author(s):  
Kai Song Zhang ◽  
Luo Zhong ◽  
Xuan Ya Zhang
Keyword(s):  
Image Restoration ◽  
Sparse Representation ◽  
Sparse Coding ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Coefficient Matrix ◽  
Optimization Approach ◽  
Signal To Noise ◽  
Nonlinear Features ◽  
Characteristics Of Image

Sparse representation has recently been extensively studied in the field of image restoration. Many sparsity-based approaches enforce sparse coding on patches with certain constraints. However, extracting structural information is a challenging task in the field image restoration. Motivated by the fact that structured sparse representation (SSR) method can capture the inner characteristics of image structures, which helps in finding sparse representations of nonlinear features or patterns, we propose the SSR approach for image restoration. Specifically, a generalized model is developed using structured restraint, namely, the group [Formula: see text]-norm of the coefficient matrix is introduced in the traditional sparse representation with respect to minimizing the differences within classes and maximizing the differences between classes for sparse representation, and its applications with image restoration are also explored. The sparse coefficients of SSR are obtained through iterative optimization approach. Experimental results have shown that the proposed SSR technique can significantly deliver the reconstructed images with high quality, which manifest the effectiveness of our approach in both peak signal-to-noise ratio performance and visual perception.

scholarly journals Pansharpening of WorldView-2 Data via Graph Regularized Sparse Coding and Adaptive Coupled Dictionary

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3586
Author(s):  
Wenqing Wang ◽  
Han Liu ◽  
Guo Xie
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Sparse Coding ◽  
Spectral Response ◽  
Objective Evaluation ◽  
Coefficient Matrix ◽  
Image Patch ◽  
Subjective Analysis ◽  
High Resolution Image ◽  
Sparse Coefficient

The spectral mismatch between a multispectral (MS) image and its corresponding panchromatic (PAN) image affects the pansharpening quality, especially for WorldView-2 data. To handle this problem, a pansharpening method based on graph regularized sparse coding (GRSC) and adaptive coupled dictionary is proposed in this paper. Firstly, the pansharpening process is divided into three tasks according to the degree of correlation among the MS and PAN channels and the relative spectral response of WorldView-2 sensor. Then, for each task, the image patch set from the MS channels is clustered into several subsets, and the sparse representation of each subset is estimated through the GRSC algorithm. Besides, an adaptive coupled dictionary pair for each task is constructed to effectively represent the subsets. Finally, the high-resolution image subsets for each task are obtained by multiplying the estimated sparse coefficient matrix by the corresponding dictionary. A variety of experiments are conducted on the WorldView-2 data, and the experimental results demonstrate that the proposed method achieves better performance than the existing pansharpening algorithms in both subjective analysis and objective evaluation.

Analysis of Sparse Representation and Blind Source Separation

2004 ◽  
Vol 16 (6) ◽  
pp. 1193-1234 ◽  
Author(s):  
Yuanqing Li ◽  
Andrzej Cichocki ◽  
Shun-ichi Amari
Keyword(s):  
Sparse Representation ◽  
Blind Source Separation ◽  
Clustering Algorithm ◽  
Source Separation ◽  
Coefficient Matrix ◽  
Data Matrix ◽  
Programming Algorithm ◽  
Optimization Approach ◽  
Basis Matrix ◽  
Two Stage

In this letter, we analyze a two-stage cluster-then-l1-optimization approach for sparse representation of a data matrix, which is also a promising approach for blind source separation (BSS) in which fewer sensors than sources are present. First, sparse representation (factorization) of a data matrix is discussed. For a given overcomplete basis matrix, the corresponding sparse solution (coefficient matrix) with minimum l1 norm is unique with probability one, which can be obtained using a standard linear programming algorithm. The equivalence of the l1—norm solution and the l0—norm solution is also analyzed according to a probabilistic framework. If the obtained l1—norm solution is sufficiently sparse, then it is equal to the l0—norm solution with a high probability. Furthermore, the l1—norm solution is robust to noise, but the l0—norm solution is not, showing that the l1—norm is a good sparsity measure. These results can be used as a recoverability analysis of BSS, as discussed. The basis matrix in this article is estimated using a clustering algorithm followed by normalization, in which the matrix columns are the cluster centers of normalized data column vectors. Zibulevsky, Pearlmutter, Boll, and Kisilev (2000) used this kind of two-stage approach in underdetermined BSS. Our recoverability analysis shows that this approach can deal with the situation in which the sources are overlapped to some degree in the analyzed

Application of Constraint CMA Blind Equalization Algorithm in the Medical Image Restoration

2012 ◽  
Vol 263-266 ◽  
pp. 2109-2112
Author(s):  
Jin Zhang ◽  
Ya Jie Mao ◽  
Li Yi Zhang ◽  
Yun Shan Sun
Keyword(s):  
Image Restoration ◽  
Cost Function ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Blind Equalization ◽  
Optimal Solution ◽  
The Novel ◽  
Constant Modulus ◽  
Signal To Noise ◽  
Equalization Algorithm

A constraint constant module blind equalization algorithm for medical image based on dimension reduction was proposed. The constant modulus cost function applied to medical image was founded. In order to improve the effect of image restoration, a constraint item was introduced to restrict cost function, and it guarantees that the algorithm converge the optimal solution. Compared to the traditional methods, the novel algorithm improves peak signal to noise ratio and restoration effects. Computer simulations demonstrate the effectiveness of the algorithm.

Sparse Coding for Improved Signal-to-Noise Ratio in MRI

2014 ◽  
pp. 258-265 ◽  
Author(s):  
Fuleah A. Razzaq ◽  
Shady Mohamed ◽  
Asim Bhatti ◽  
Saeid Nahavandi
Keyword(s):  
Sparse Coding ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio

The Image Restoration Method Based on Patch Sparsity Propagation in Big Data Environment

2018 ◽  
Vol 22 (7) ◽  
pp. 1072-1076
Author(s):  
Kun Ling Wang ◽  
Keyword(s):  
Big Data ◽  
Image Restoration ◽  
Sparse Coding ◽  
Design Method ◽  
Image Data ◽  
Propagation Analysis ◽  
Blurred Image ◽  
Data Environment ◽  
Restoration Method ◽  
Characteristics Of Image

The traditional image restoration method only uses the original image data as a dictionary to make sparse representation of the pending blocks, which leads to the poor adaptation of the dictionary and the blurred image of the restoration. And only the effective information around the restored block is used for sparse coding, without considering the characteristics of image blocks, and the prior knowledge is limited. Therefore, in the big data environment, a new method of image restoration based on structural coefficient propagation is proposed. The clustering method is used to divide the image into several small area image blocks with similar structures, classify the images according to the features, and train the different feature types of the image blocks and their corresponding adaptive dictionaries. According to the characteristics of the restored image blocks, the restoration order is determined through the sparse structural propagation analysis, and the image restoration is achieved by sparse coding. The design method is programmed, and the image restoration in big data environment is realized by designing the system. Experimental results show that the proposed method can effectively restore images and has high quality and efficiency.

scholarly journals Simultaneous Patch-Group Sparse Coding with Dual-Weighted ℓp Minimization for Image Restoration

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1205
Author(s):  
Jiachao Zhang ◽  
Ying Tong ◽  
Liangbao Jiao
Keyword(s):  
Image Restoration ◽  
Sparse Representation ◽  
Sparse Coding ◽  
Image Inpainting ◽  
Perceptual Quality ◽  
Novel Approach ◽  
Smooth Effects ◽  
Convex Regularization ◽  
Generalized Iteration ◽  
Patch Group

Sparse coding (SC) models have been proven as powerful tools applied in image restoration tasks, such as patch sparse coding (PSC) and group sparse coding (GSC). However, these two kinds of SC models have their respective drawbacks. PSC tends to generate visually annoying blocking artifacts, while GSC models usually produce over-smooth effects. Moreover, conventional ℓ1 minimization-based convex regularization was usually employed as a standard scheme for estimating sparse signals, but it cannot achieve an accurate sparse solution under many realistic situations. In this paper, we propose a novel approach for image restoration via simultaneous patch-group sparse coding (SPG-SC) with dual-weighted ℓp minimization. Specifically, in contrast to existing SC-based methods, the proposed SPG-SC conducts the local sparsity and nonlocal sparse representation simultaneously. A dual-weighted ℓp minimization-based non-convex regularization is proposed to improve the sparse representation capability of the proposed SPG-SC. To make the optimization tractable, a non-convex generalized iteration shrinkage algorithm based on the alternating direction method of multipliers (ADMM) framework is developed to solve the proposed SPG-SC model. Extensive experimental results on two image restoration tasks, including image inpainting and image deblurring, demonstrate that the proposed SPG-SC outperforms many state-of-the-art algorithms in terms of both objective and perceptual quality.

Research on signal-to-noise ratio characteristics and image restoration for wavefront coding

2015 ◽  
Author(s):  
Yijian Wu ◽  
Yuejin Zhao ◽  
Xiaohu Guo ◽  
Liquan Dong ◽  
Wei Jia ◽  
...  
Keyword(s):  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Wavefront Coding ◽  
Noise Ratio
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