Method for removing salt-pepper noises in images

Abstract

The invention relates to a method for removing salt-pepper noises in images, which comprises the following steps: A, identifying whether pi, j is a salt-pepper noise point; B, when the pi, j is a salt-pepper noise point, acquiring a 3*3 local window omega 3 centering on the pixel pi, j, and estimating a noise density d (omega 3) of the omega 3; C, when the noise density d (omega 3) obtained in the step B is less than 9, defining a filter output ri, j of the pi, j as follows: omega (fs, t, fi, j) = exp (-beta |fs, t - fi, j |); and D, when the noise density d (omega 3) obtained in the step B is equal to 9, defining the filter output ri, j of the pi, j as the steps in the specification. The invention aims to provide a method for removing salt-pepper noises in images, through which salt-pepper noise points in an area adjacent to some pixel can be removed better, so that adverse effects on the filter output of center pixels caused by noise points in local windows us reduced, and a more accurate filter output is obtained by using a new direction weighted average strategy.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a method for denoising and restoring an image polluted by salt and pepper noise. Background technique [0002] Salt and pepper noise is a common noise that can be caused by a variety of factors, such as sensor hardware failures or communication channel interference. Salt and pepper noise is manifested as black (grayscale 0) or white (grayscale 255) pixels that are incompatible with neighboring pixels in the image. There is a sudden change in gray level between the noise point and the adjacent pixel point, resulting in a large gradient value, which makes it easy to be confused with the edge point, which brings great difficulties to image analysis, especially edge detection. [0003] Switching median filter is a recent research hotspot in the field of image salt and pepper denoising. The so-called switch means to detect the salt and pepper noise points in the image first, ...

AI Technical Summary

Problems solved by technology

[0008] (1) The local window itself does not contain edges. In this case, the so-called optimal direction is not the real edge direction. Only considering the adjacent pixels in this direction will make it impossible to fully describe the grayscale characteristics of the neighborhood.

[0009] (2) MDWMF uses the extreme gray value (0 and 255) conditions to remove the noise points of the neighbors. This method is rough, and the non-noise points with the extreme gray value may also be removed.

It can be seen that the best direction may not be the real edge direction, and there is a problem with MDWMF's approach

Images