Non-local mean value image denoising method based on filter window and parameter adaption

Abstract

The invention discloses a non-local mean value image denoising method based on a filter window and parameter adaption. According to the invention, firstly, noise is detected, and a noise calibration matrix is established according to a detection result; the size of the noise calibration matrix is consistent with the size of an image, and the matrix value at a corresponding position of each noise point is set to be 1, and the matrix value at a corresponding position of each non-noise point is set to be 0. Then, each pixel of a noise image is successively taken as a reference point, and centric to the point, a predetermined number of non-noise reference points are taken in a counterclockwise direction to be involved in computation. Finally adaptive weighting parameters are determined according to the locations of the reference points, and a weighted result is calculated and a restored pixel value is obtained; the corresponding element in the noise calibration matrix is set to be 0 and the pixel point after denoising can be used as a reference point of other noise points. Compared with traditional image denoising methods, the method provided by the invention is added with the noise detection and noise point screening, thus improving algorithm accuracy, changing a reference point selection window and improving algorithm adaptability.

Description

technical field [0001] The invention relates to an image denoising method, in particular to a nonlocal mean (Nonlocal Means, NLM) denoising method based on filter window and parameter self-adaptation, which belongs to the field of digital image preprocessing. This method realizes the effective removal of image noise, and at the same time preserves the information of the image itself as much as possible, especially the details and structure information. Through the adaptive filtering window and parameters, the applicability and intelligence of the method are enhanced, and the denoising process is simplified. It can be applied to an automatic image processing system. Background technique [0002] As the importance of information continues to increase, digital images, as an important information carrier, are widely used in all walks of life in modern society. People can intuitively obtain information from images, but due to the interference of external signals during image ac...

AI Technical Summary

Problems solved by technology

People can intuitively obtain information from images, but due to the interference of external signals during image acquisition and transmission, or due to the defects of the imaging system itself, image noise will inevitably form.

Images