Image synthesis restoration method based on local structure features

Abstract

The present invention relates to an image synthesis restoration method based on local structure features, comprising the following steps: inputting an image to be restored; initializing confidence coefficients of all pixel points in the image to be restored; selecting a template block of each pixel point by respectively taking each pixel point on a damaged boundary of the image to be restored as the center; calculating the priority of the template block where each pixel point on the damaged boundary of the image to be restored locates, and performing descending sort; searching an area which has the highest similarity with the template block having the greatest priority from an undamaged area in the image to be restored to serve as an optimal matching block; copying pixel values in the optimal matching block to the template block; and updating the confidence coefficients and returning to execute corresponding steps until the image to be restored is completely restored. The image synthesis restoration method based on local structure features provided in the present invention can improve image restoration accuracy for a damaged image with a complicated texture structure. The present invention also relates to an image synthesis restoration system based on local structure features.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image synthesis repair method based on local structural features. Background technique [0002] Image inpainting is one of the main contents of image processing, mainly including structural image inpainting and texture image inpainting. Inpainting methods can be roughly divided into two categories: one is the image inpainting method based on partial differential equations, and the other is based on texture synthesis. image restoration methods. [0003] In the image restoration method based on texture synthesis, it can be divided into two categories: one is the method based on decomposition, and the other is the method of texture synthesis and restoration based on samples, and in the method of texture synthesis and restoration based on samples, Its representative algorithm is the Criminisi algorithm based on the isoluminescence line drive. The Criminisi algorithm first determin...

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Problems solved by technology

However, as the repair process proceeds, the data items set by the priority will drop rapidly, while the confidence item will start to increase, making the calculation of the priority unreliable, resulting in a wrong filling order; and the global search method adopted , the time complexity will be relatively high; in addition, the matching criterion only uses the color distance to determine the optimal matching block, which will eventually generate multiple target blocks, and for these multiple target blocks, the Criminisi algorithm uses random selection of target blocks. This increases the error matching rate of the image

The final defect in the image is the lack of fidelity of the image structure and the poor quality of image restoration

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