Large-scale image restoration achieving method based on image gradient prior model

Abstract

The invention relates to a large-scale image restoration achieving method based on an image gradient prior model. The method includes: reading-in information of an image to be restored, building an image noise model, a non-local mean value spatial domain probability model and an image gradient prior probability model according to the information of the image to be restored, building a Bayesian posterior probability model and calculating a posterior probability estimation value, confirming a value of the current pixel according to the posterior probability estimation value and saving the value of the current pixel, and then restoring the next pixel until restoration of the whole image is finished. The large-scale image restoration achieving method based on the image gradient prior model is used for solving the problem that texture boundary consistency is poor in the image restoration process in the prior art, making full use of redundancy of video images in a spatial domain, combining cooperative constraints of the non-local mean value spatial domain probability model and the image gradient prior probability model, and effectively restoring the information of images with large-scale damages. The large-scale image restoration achieving method based on the image gradient prior model is convenient to use and has a wider application range.

Description

technical field [0001] The invention relates to the field of image processing, in particular to the field of large-scale image restoration, and specifically refers to a method for realizing large-scale image restoration based on an image gradient prior model. Background technique [0002] Image inpainting is the process of repairing damaged images. With the development of digital technology, more and more image data are collected, stored and processed in a digital way. In the process of using digital images, we often encounter partial information loss of digital images. For example, after digitization of old film, there are often scratches, plaques and other damages in the images, and some data loss occurs in digital images. When editing an image, removing or moving text and objects in the image will result in a blank area of ​​the image. After the original characters in the image are removed, a large block of image data will be left blank. The absence of such information ...

AI Technical Summary

Problems solved by technology

However, the biggest disadvantage of these methods is that they fail to consider the texture consistency between the repaired area and the known area, and the optimal matching block obtained during the block matching priority calculation process often cannot obtain better visual effects, that is, the coherence of the boundary sex

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