Image residual signal based non-local mean value image de-noising method

Abstract

The invention discloses an image residual signal based non-local mean value image de-noising method for mainly solving the problem that detailed information of partial images is lost when traditional non-local mean value image de-noising method is used for removing noise. The image residual signal based non-local mean value image de-noising method comprises the de-noising steps of: (1) performing original non-local mean value filtration on an input noise-including natural image to obtain a primary filtering result picture and a method noise picture; (2) judging which region each pixel point in the method noise picture belongs to; (3) extracting image residual information based on different regions which each pixel point belongs to so as to obtain a residual information picture; (4) obtaining a de-noising reference picture by utilizing the residual information picture and the primary filtering result picture; (5) calculating a new weight in the de-noising reference picture, and performing non-local mean value filtration on the noise-including natural image by utilizing the new weight to obtain an estimated value of each pixel point; and (6) replacing grey values of all the pixel points in the noise-including natural image by using the estimated values of all the pixel points to obtain a de-noised image. The image residual signal based non-local mean value image de-noising method, disclosed by the invention, has the advantages of obtaining better de-noising effects, and being used for de-noising natural images.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a denoising method, which can be used for denoising processing of natural images. Background technique [0002] During the process of image generation, transmission and recording, it is often disturbed by various noises. Generally speaking, images in reality are images with noise. Before high-level processing such as edge detection and image segmentation, image denoising is a very necessary preprocessing step. People have done a lot of research on the causes of image noise and the corresponding noise models, and found that Gaussian white noise with zero mean and different variances can be used as its model for most common image noises. [0003] Image denoising methods can generally be divided into spatial domain filtering methods and frequency domain filtering methods. Spatial domain filtering methods mainly include: average filtering, median filtering, bilateral filte...

AI Technical Summary

Problems solved by technology

From the method noise map, it can be clearly seen that part of the image information is removed together as noise, and the filtered information contained in the method noise map obtained by filters with different parameters is different, which leads to image denoising results. not good enough

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