Image restoration method based on curvature driven diffusion

Abstract

The invention discloses an image restoration method based on curvature-driven diffusion. A nonnegative, smooth and monotonically decreasing guiding function g(s) is introduced through a nonlinear diffusion term portion of a curvature driven diffusion (CDD) model, a self-adaptive CDD model is designed, wherein g(0) is equal to 1, a parameter s is a gradient module value, m is a regulatory factor of a margin guiding function decreasing speed, k is a threshold parameter, and median is mean value operation. The guiding function g(s) changes with change of the parameter s, the diffusion factor changes, and therefore restoration intensity self-adaptive control of an image smooth area and a texture and margin area is achieved. The self-adaptive model is better than a total variation (TV) model and the CDD model when a large scale damaged area is restored, image margin and detail information can be kept well, and vision effect is good.

Description

technical field [0001] The invention belongs to the field of digital image processing, and in particular relates to an image restoration method based on curvature-driven diffusion that can protect small but smooth isoluminance lines, retain image edges and detail information, have good restoration effect and high calculation efficiency. Background technique [0002] In recent years, with the continuous development of digital media technology, image restoration technology has been widely used, such as the protection of cultural relics and old photos, the restoration of image lossy compression, the error concealment of images and videos after network transmission, and the redundancy in images. Removal of remaining objects, etc. However, digital image restoration is a pathological problem because the original information has been destroyed and there is no standard image as a reference. Among the existing restoration methods, image restoration based on partial differential equa...

AI Technical Summary

Problems solved by technology

Although the CDD model satisfies the principle of visual connectivity and has a good repair effect on small-scale damaged images, it requires a large amount of calculation for damaged areas with complex textures, and there is a ladder effect.

The main problems of the existing technology are summarized as follows: first, the iteration time is long, the calculation complexity is high, and the convergence speed is slow; second, the typical isotropic diffusion easily leads to edge blurring, while the CDD model diffusion is easy to produce Unnecessary false edges and staircase effects

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