AN EFFECTIVE IMAGE DEBLURRING SCHEME USING CLUSTER BASED SPARSE REPRESENTATION

Sparse based representation is being used extensively for image restoration. The dictionary learningthrough patch extraction is central to the sparse based schemes. In the process of dictionary learning,a large number of patches will be extracted from high quality images and dictionary will be formed.Hence, over-complete dictionaries will be built. To overcome the complexity associated with overcompletedictionaries many schemes were proposed. Of them, the adaptive sparse domain is thepopular one. Many variations of adaptive sparse domain schemes were proposed. Selection of obviouspatches is common to all. In all these schemes, individual patches will be considered as the basic entityand will be used. This is the reason for the complexity involved in sparse representation. In this paper,to avoid the complexity, the patches are grouped according to the similarity among the patches. Inaddition to reduce the complexity the proposed cluster based scheme considers the self-similarity ofthe patches involved. Hence better performance with less complexity is possible with the proposedschemes. In the process of testing, in addition to uniform blur and Gaussian blur, a combination of thetwo blurs is also considered.

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A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP

Algorithms ◽

10.3390/a12080174 ◽

2019 ◽

Vol 12 (8) ◽

pp. 174

Author(s):

Sun ◽

Zhang ◽

Li ◽

Meng

Keyword(s):

Image Reconstruction ◽

Radiation Dose ◽

Image Restoration ◽

Sparse Representation ◽

Iterative Reconstruction ◽

Model Parameters ◽

Projection Data ◽

Ct Image ◽

High Quality ◽

Ct Image Reconstruction

Computed tomography (CT) image reconstruction and restoration are very important in medical image processing, and are associated together to be an inverse problem. Image iterative reconstruction is a key tool to increase the applicability of CT imaging and reduce radiation dose. Nevertheless, traditional image iterative reconstruction methods are limited by the sampling theorem and also the blurring of projection data will propagate unhampered artifact in the reconstructed image. To overcome these problems, image restoration techniques should be developed to accurately correct a wide variety of image degrading effects in order to effectively improve image reconstruction. In this paper, a blind image restoration technique is embedded in the compressive sensing CT image reconstruction, which can result in a high-quality reconstruction image using fewer projection data. Because a small amount of data can be obtained by radiation in a shorter time, high-quality image reconstruction with less data is equivalent to reducing radiation dose. Technically, both the blurring process and the sparse representation of the sharp CT image are first modeled as a serial of parameters. The sharp CT image will be obtained from the estimated sparse representation. Then, the model parameters are estimated by a hierarchical Bayesian maximum posteriori formulation. Finally, the estimated model parameters are optimized to obtain the final image reconstruction. We demonstrate the effectiveness of the proposed method with the simulation experiments in terms of the peak signal to noise ratio (PSNR), and structural similarity index (SSIM).

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Ensemble Dictionary Learning for Single Image Deblurring via Low-Rank Regularization

Sensors ◽

10.3390/s19051143 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1143 ◽

Cited By ~ 1

Author(s):

Jinyang Li ◽

Zhijing Liu

Keyword(s):

Sparse Representation ◽

Dictionary Learning ◽

Clustering Algorithms ◽

Solution Space ◽

Image Deblurring ◽

Statistical Technique ◽

Low Rank ◽

Single Image ◽

Norm Minimization ◽

Visual Artifacts

Sparse representation is a powerful statistical technique that has been widely utilized in image restoration applications. In this paper, an improved sparse representation model regularized by a low-rank constraint is proposed for single image deblurring. The key motivation for the proposed model lies in the observation that natural images are full of self-repetitive structures and they can be represented by similar patterns. However, as input images contain noise, blur, and other visual artifacts, extracting nonlocal similarities only with patch clustering algorithms is insufficient. In this paper, we first propose an ensemble dictionary learning method to represent different similar patterns. Then, low-rank embedded regularization is directly imposed on inputs to regularize the desired solution space which favors natural and sharp structures. The proposed method can be optimized by alternatively solving nuclear norm minimization and l 1 norm minimization problems to achieve higher restoration quality. Experimental comparisons validate the superior results of the proposed method compared with other deblurring algorithms in terms of visual quality and quantitative metrics.

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Uniform and Non-Uniform Single Image Deblurring Based on Sparse Representation and Adaptive Dictionary Learning

The International journal of Multimedia & Its Applications ◽

10.5121/ijma.2014.6104 ◽

2014 ◽

Vol 6 (1) ◽

pp. 47-60 ◽

Cited By ~ 1

Author(s):

Ashwini M. Deshpande ◽

Suprava Patnaik

Keyword(s):

Sparse Representation ◽

Dictionary Learning ◽

Image Deblurring ◽

Single Image

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A blind image deblurring algorithm based on relative gradient and sparse representation

Modern Physics Letters B ◽

10.1142/s0217984918400870 ◽

2018 ◽

Vol 32 (34n36) ◽

pp. 1840087 ◽

Cited By ~ 3

Author(s):

Qiwei Chen ◽

Yiming Wang

Keyword(s):

Image Reconstruction ◽

Image Restoration ◽

Sparse Representation ◽

Kernel Estimation ◽

Image Deblurring ◽

Blind Image Deblurring ◽

Overcomplete Dictionary ◽

Blur Kernel ◽

Blind Image

A blind image deblurring algorithm based on relative gradient and sparse representation is proposed in this paper. The layered method restores the image by three steps: edge extraction, blur kernel estimation and image reconstruction. The positive and negative gradients in texture part have reversal changes, and the edge part that reflects the image structure has only one gradient change. According to the characteristic, the edge of the image is extracted by using the relative gradient of image, so as to estimate the blur kernel of the image. In the stage of image reconstruction, in order to overcome the problem of oversize of the image and the overcomplete dictionary matrix, the image is divided into small blocks. An overcomplete dictionary is used for sparse representation, and the image is reconstructed by the iterative threshold shrinkage method to improve the quality of image restoration. Experimental results show that the proposed method can effectively improve the quality of image restoration.

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