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Image reconstruction method based on group sparsity coefficient estimation

An image reconstruction and group sparse technology, applied in image enhancement, image data processing, computing and other directions, can solve the problems of difficult to reflect the detailed texture information of the image, unable to achieve accurate estimation of sparse coefficients, etc.

Inactive Publication Date: 2016-03-23
CHONGQING UNIV
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Problems solved by technology

However, the threshold filtering method based on sparse coefficients cannot accurately estimate the sparse coefficients, and it is difficult to reflect the detailed texture information of the image in the obtained results.

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  • Image reconstruction method based on group sparsity coefficient estimation
  • Image reconstruction method based on group sparsity coefficient estimation
  • Image reconstruction method based on group sparsity coefficient estimation

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Embodiment Construction

[0041] refer to figure 1 , the present invention is based on the image reconstruction method of group sparse coefficient estimation, and concrete steps comprise as follows:

[0042] Step 1. Sparse representation of groups of similar image blocks

[0043] First, after extracting the image block, use formula (13) to obtain its Euclidean distance:

[0044] d ( x i , x j ) = | | x i - x j | | 2 2 Formula (13)

[0045] where x j is any image block in the search window, is the two-norm square, d(xi ,x j ) indicates that the similarity between the two image blocks is smaller, and then the similar image block set X of L similar image block sets is obtained by formula ...

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Abstract

The invention discloses an image reconstruction method based on group sparsity coefficient estimation. The method belongs to the technical field of digital image processing and is an image reconstruction method based on similar block set sparsity coefficient estimation, wherein similar image blocks are searched by an Euclidean distance at first; partial and non-partial sparse representation is carried out to a similar image block set, so that the sparser and more accurate coefficient is obtained; a reconstruction model is solved further by a Bergman iteration algorithm; and the sparsity coefficient is estimated according to a linear minimum mean square error rule, so that the accurate estimation of the small coefficient containing image texture detailed information is ensured. The method disclosed by the invention has the advantages that the linear minimum mean square error estimation is carried out to the similar image block set sparsity representation coefficient, so that obvious effects are obtained in repair, deblurring and other aspects; a reconstructed image will also have the more abundant detailed information; overall visual effects become clearer; and the method can be used in the repair and the deblurring of the optical images.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and particularly relates to an image reconstruction method based on group sparse coefficient estimation, which is used for optical image restoration and deblurring processing. Background technique [0002] In recent years, sparse representation and dictionary learning have become a research hotspot and are widely used in image processing and computer vision, such as image denoising, inpainting, and color editing. The sparse representation of the image is reconstructed by linear combination of the image with a complete dictionary, and the purpose of sparse representation is achieved by using a limited number of reconstructed samples. [0003] Traditional image sparse representation takes advantage of the sparse property of real images in the transform domain, and effectively realizes the sparse representation of real image information in the transform domain. The key of this meth...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/73
Inventor 刘书君吴国庆沈晓东张新征曹建鑫
Owner CHONGQING UNIV
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