Image salt-and-pepper noise removal method based on mean value in iteration switch

Abstract

The invention provides animage salt-and-pepper noise removal method based on a mean value in an iteration switch. By setting a simple noise detection operator, a point-by-point self-adaptive filtering window is conveniently constructed, and noise points are rapidly judged. Due to the fact that pixel gray level values in small areas of an image have high relevance, by means of the small-size 3*3 pixel filtering window, detail information of the image can be effectively reserved, and meanwhile the false detection problem caused by the simple noise detection operator becomes very small. High-concentration salt-and-pepper noise is removed through a layer-by-layer switch filtering method instead of a large-size filtering window, and the noise can be eliminated step by step. Under the high-concentration noise environment, the gray level values of the noise points are replaced with mid values in grey level values of non-noise points, in all directions, in a long distance, mean value filtering is conducted on filtered pixels, and deviation is further reduced. By means of the method, the aim of reserving existing detail information of the image while effectively removing noise is achieved.

Description

Technical field: [0001] The invention relates to an image noise filtering method, in particular to an image salt-and-pepper noise removal method based on an iterative switching mean value, which belongs to the field of image denoising. Background technique: [0002] Many existing electronic products are basically equipped with functions such as image acquisition, transmission and reception. However, in the process of image acquisition and transmission, they are often affected by problems such as the external environment, the quality of sensor components, and the damage of transmission channels, and various degrees of pulses are introduced. Noise, among which salt and pepper noise is the most common impulsive noise. Even a small noise concentration will have a serious impact on the post-processing of the image. Therefore, how to effectively remove noise and retain the details of the original image is a very meaningful research work, and has a wide range of application values...

AI Technical Summary

Problems solved by technology

[0003] The salt and pepper noise removal methods currently used can be roughly divided into three categories: the first category is the use of median filters and improved median filters, this type of method uniformly filters the noise points and non-noise points of the original image, and High-concentration noise adopts a larger filter window, which makes this type of method unable to effectively remove noise; the second type uses a switch-based median filter, and the feature of this type of method is to set a noise detection operator. However, this method uses a gradually larger filter window when removing high-density noise, which cannot effectively retain the basic details of the original image; the third category is Use other filtering methods based on switch mode, such as non-local mean value, interpolation algorithm, etc., and this type of method needs to set different parameters, which makes the process of noise removal method more complicated and requires more processing time, which is not practical

[0004] In summary, according to some existing salt and pepper noise filtering methods, there is a lack of an adaptive, simple, fast and efficient filtering method

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