Impulse noise inhibition method based on recursion Gauss maximum likelihood estimation of confidence similarity

Abstract

The invention discloses an impulse noise inhibition method based on recursion Gauss maximum likelihood estimation of confidence similarity. The method includes the steps that firstly, assuming that, pixels with the gray value of 0 and 255 are pixels polluted by noise, a mask image is obtained, and noise density is calculated; secondly, the restored value of each pixel is determined in a cyclic mode, if a noise point exists, a weighed estimated value is assigned to a target restored image, or else, a current pixel value is assigned to the target restored image, a current pixel is calculated to be a window weight matrix, the estimated value of the current pixel is calculated through the Gauss maximum likelihood estimation, the gray values of pixels which are not polluted by noise in the image are calculated again in each time of iteration, the peak signal to noise ratio of the gray values of the pixels to the gray values of pixels at corresponding positions in a noise image is calculated, and if the peak signal to noise ratio is not increased any more, iteration is stopped. According to the impulse noise inhibition method, impulse noise is effectively inhibited, meanwhile, local details are stored so that a local structure has the better contrast ratio, and the better image restoring effect is achieved.

Description

technical field [0001] The invention relates to the field of computer image processing, in particular to an impulse noise suppression method based on recursive Gaussian maximum likelihood estimation of confidence similarity. Background technique [0002] Impulse noise is caused by sensor damage, failure, or timing errors during signal acquisition. The median filter is an effective method for dealing with impulse noise. Impulse noise suppression usually consists of two steps—noise detection and noise removal. The standard median filter (Standard Median Filter, SMF) is not very effective when the impulse noise density is very high, and when the impulse noise density is greater than 50%, the image processed by the median filter often loses The restored image is severely degraded due to loss of detail and newly introduced artifacts. [0003] So far, many methods have been invented to overcome the shortcomings of the standard median filter. The adaptive median filter (Adaptiv...

AI Technical Summary

Problems solved by technology

[0004] In terms of suppressing impulse noise, the median method is prone to blurring of details and step artifacts, resulting in image degradation

This problem is more severe for high-density noise cases and structural ambiguities

The reason may be attributed to the fact that the median method only uses the median information obtained from a window pixel as a median filter and does not take advantage of the structural continuity information

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