Image denoising method based on secondary noise point detection

Abstract

The invention relates to the field of image processing, and provides a novel self-adaptive secondary noise point detection method based on direction information. The method can be used for effectively reducing the erroneous judgment probability of non-noise points and removing impulse noise in images more effectively, and the denoising robustness on noise with different strengths is stronger. To this end, the technical scheme adopted in the invention is as follows: an image denoising method based on secondary noise point detection comprises the following steps: at first, determining an impulse noise density condition, counting all gray values in an image, calculating the number of pixel points whose gray values are 0 or 255, dividing the number by the number of total pixel points to obtain an approximate noise probability density pa, when pa is larger than or equal to s, filtering all points whose gray values are 0 or 255, when pa is smaller than s, carrying out secondary noise point detection on these points, and the detection principle is mainly based on direction features of edge information in the image; and then self-adaptive filtering is carried out. The image denoising method based on secondary noise point detection provided by the invention is mainly applied to image processing occasions.

Description

technical field [0001] The invention relates to the field of image processing, in particular to the problem of distinguishing, judging and filtering out noise points when removing salt and pepper noise from images. Specifically, it involves an image noise reduction method based on secondary noise detection. technical background [0002] When performing image target recognition and tracking, the image collected by the camera will inevitably be disturbed by various noises during the imaging, digitization, and transmission processes, and the image quality will often degrade unsatisfactorily, affecting the image. visual effects. Usually, these noises degrade the image, manifested as blurred image and submerged features, which will be unfavorable to image analysis and make the obtained image lower in quality. It is difficult to directly identify and track targets on such images. It is very important to suppress various interference signals that degrade the image, enhance the u...

AI Technical Summary

Problems solved by technology

These algorithms keep the edges and details of the image better in the process of denoising, but there are corresponding shortcomings, such as large amount of calculation, poor denoising effect of high-density noise, poor versatility, etc.

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