Weight window self-adaptation non-local mean image denoising method

Abstract

The invention discloses a weight window self-adaptation non-local mean image denoising method. According to the weight window self-adaptation non-local mean image denoising method, the sizes of weight windows can be controlled in a self-adaptation mode according to image local structure characteristics, noise is suppressed while an edge structure is protected, and therefore the image quality is remarkably improved. The method includes the following steps that first, a frame of noise image is initialized and read in; second, a structure tensor matrix is built; third, according to the built structure tensor matrix, edge structure indicators are built, and the characteristics of the area where pixel dots are located are positioned; fourth, the areas of the image are classified through the edge structure indicators; fifth, according to the type of the area to which each pixel dot belongs, the size of the adjacent area of each pixel dot is determined; sixth, according to the determined size of the adjacent area of each pixel dot, a similarity metric function between the adjacent areas is built; seventh, S dots with highest similarity are screened; eighth, a denoising model is built, and a denoised image is acquired.

Description

technical field [0001] The invention belongs to the field of image processing and relates to a weight window self-adaptive non-local mean value image denoising method. Background technique [0002] Image denoising technology is a key issue in image processing and low-level vision. It is the basis for subsequent pattern recognition and high-level understanding. It has a wide range of application requirements. This technology can be applied to many fields such as traffic monitoring, military, and medicine. Therefore, improving image quality through image denoising technology has important theoretical significance and practical value, and has received great attention from academic and commercial circles at home and abroad. The non-local mean method is a new and very effective image denoising method. It uses the characteristics of many similar image blocks in most images, and searches for blocks with similar gray levels in the image for matching to estimate the noise points. va...

AI Technical Summary

Problems solved by technology

The selection of the weight window size of the non-local mean method is very important. Choosing a larger weight window can easily remove noise, but at the same time it will lead to blurring of the edge structure. Conversely, choosing a smaller weight window can Edge structures are well preserved, but noise is not well suppressed

Images