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An image restoration method based on gradient sparsity and non-local similarity information

A non-local similarity and sparsity technology, applied in image enhancement, image data processing, instruments, etc., can solve problems such as image quality degradation, noise pollution, image understanding and pattern recognition difficulties

Inactive Publication Date: 2019-02-19
THE 28TH RES INST OF CHINA ELECTRONICS TECH GROUP CORP
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Problems solved by technology

[0002] During the imaging, transmission and storage of images, due to the inherent physical limitations of imaging equipment and the limitations of external environmental conditions, degradation phenomena such as noise pollution and blurring will inevitably occur, resulting in a decline in image quality
This kind of image degradation will bring considerable difficulties to its subsequent applications such as image understanding and pattern recognition.

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  • An image restoration method based on gradient sparsity and non-local similarity information
  • An image restoration method based on gradient sparsity and non-local similarity information
  • An image restoration method based on gradient sparsity and non-local similarity information

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Embodiment

[0120] The present invention provides a two-stage image restoration method combining gradient sparsity and non-local similarity information, which includes an initialization process of an image restoration system and a two-stage image restoration process:

[0121] 1. The initialization process of the image restoration system is:

[0122] (1) Enter a picture size of M 1 ×N 1 Blurred image g, size M 2 ×N 2 Point spread function h, size M 1 ×N 1 Initialize the image to be restored u 0 , Initialize the augmented Lagrangian multiplier J 0 ;

[0123] Where: M 1 ,N 1 Represent the number of rows and columns of the image, respectively, M 2 ,N 2 It respectively represents the number of rows and columns of the point spread function.

[0124] (2) The image restoration system parameters that need to be initialized include: Lagrangian multiplier calculation step parameter ξ = 1, non-local similar block search area size r n ×r n =25×25, non-local similar block size r p ×r p =5×5, gradient sparsity c...

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Abstract

The invention discloses an image restoration method based on gradient sparsity and non-local similarity information, comprising a first stage of image non-blind restoration based on gradient sparsityand a second stage of image quality improvement based on non-local autoregressive model. The non-blind image restoration based on gradient sparsity in the first stage includes the following core steps: current gradient image auxiliary variable calculation; Current restored image update; Lagrange multiplier calculations. The invention is based on the sparsity of the image transformation domain, sothat the edge structure in the image can be recovered, and on this basis, the quality of the image restoration is further improved by utilizing the non-local similarity information in the image.

Description

Technical field [0001] The invention belongs to the field of computer digital image processing, and in particular relates to an image restoration method based on gradient sparsity and non-local similarity information. Background technique [0002] In the process of image imaging, transmission and storage, due to the inherent physical limitations of imaging equipment and external environmental conditions, degradation phenomena such as noise pollution and blur will inevitably occur, resulting in degradation of image quality. This kind of image degradation will bring considerable difficulties to its subsequent image understanding and pattern recognition applications. In order to meet the needs of practical applications, the key to image restoration is how to restore the original clear image from the degraded image, eliminate or reduce the impact of degrading factors on image quality, which has become the focus of scientific researchers and engineers in the field of image processing....

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/92G06T5/73G06T5/77
Inventor 吴蔚李晓冬王鑫鹏
Owner THE 28TH RES INST OF CHINA ELECTRONICS TECH GROUP CORP
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