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Natural image global reconstruction method based on co-sparse representation

A natural image and sparse representation technology, applied in image enhancement, image analysis, image data processing, etc., can solve problems such as high intensity and underfitting

Pending Publication Date: 2021-03-26
JIANGSU UNIV
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  • Summary
  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The root of the problem is that traditional local methods use overlapping to divide the entire image, resulting in large-scale overlapping image blocks
In addition, too much effort to solve the overfitting problem can easily lead to underfitting, so how to find a balanced strategy in the image block co-sparse representation model is the key problem to be solved in the present invention

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  • Natural image global reconstruction method based on co-sparse representation
  • Natural image global reconstruction method based on co-sparse representation
  • Natural image global reconstruction method based on co-sparse representation

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Embodiment Construction

[0040] The present invention will be further described below in conjunction with accompanying drawing.

[0041] Such as figure 1 As shown, the method of the present invention includes the following three parts (1)-(3).

[0042] (1) Propose a sparse decomposition model to realize the global reconstruction of natural image image block co-sparse representation modeling method

[0043] In order to avoid the defects of the traditional local modeling method, the present invention intends to adopt a global modeling method based on the divide-and-conquer idea to divide the image reconstruction into multiple sub-problems, and each sub-problem deals with non-overlapping image blocks covering the entire image. After averaging the reconstruction results of the sub-problems, the final image reconstruction result is obtained. The subproblem here is a division of the entire image into non-overlapping image patches that cover the entire image.

[0044] Different from the traditional method...

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Abstract

The invention discloses a natural image global reconstruction method based on co-sparse representation. The method comprises an image block co-sparse representation modeling method for realizing natural image global reconstruction through a sparse decomposition model. The method further comprises an image block co-sparsity self-adaptive fast decomposition sparse reconstruction algorithm for realizing image reconstruction. The method further comprises an alternate minimization fast decomposition dictionary learning algorithm for realizing image reconstruction. According to the method, the problems generated in the natural image reconstruction process are solved, it is ensured that image block co-sparse representation is feasible to achieve global reconstruction, high-quality reconstructionof the image is also ensured, and the method has theoretical innovation and practical significance of application.

Description

technical field [0001] The invention belongs to the field of image processing, and is precisely a method for global reconstruction of natural images, specifically a method for global reconstruction of natural images based on co-sparse representation, which is applied to the field of image reconstruction. Background technique [0002] The research based on the sparse composition model is one of the most popular topics in the past ten years. Many of its algorithms are relatively mature, and some results have appeared in industrial applications. However, another model of "sparseness" - the sparse decomposition model, It can be said that the veil has just been unveiled. The sparse decomposition model directly takes the signal x as the research object, while the sparse synthesis model uses the sparse coding z as the transition calculation signal x. In this way, the sparse decomposition model provides richer feature representation information. Compared with the sparse synthetic m...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06K9/62
CPCG06T2207/20008G06T2207/20081G06F18/28G06T5/00
Inventor 宋和平戚正晖任小龙艾泽红
Owner JIANGSU UNIV