Image denoising method for adaptive equidistant template iteration mean filtering

Abstract

The invention discloses an image denoising method for adaptive equidistant template iteration mean filtering, aimed at addressing the problems of current adaptive methods, such as poor denoising effect and poor recovery quality. The method includes the following steps: (1) using an extremum method to determine noise spots; (2) conducting sliding rotation with the middle points on 4 edges of a filtering window as start points and 4 angular points as terminal points, taking 8 symmetrical points of the 4 edges of the filtering window to construct an equidistant template (the 4 middle points and 4 angular points on the filtering window are 8 value template special cases), and forming a first equivalence template, a second equivalence template......, and conducting recursion clipping mean filtering with the equivalence templates; (3) checking whether a noise point is completed after the completion of each filtering from 3X3, and if the noise point is not completed, enlarging the filtering window and stops until 7X7, and forming adaptive filtering; (4) if the noise point is not finished, adopting iteration filtering. According to the invention, the method effectively processes noise and at the same time can better protect image details, has a higher rate of using information and has a rapid denoising speed.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image denoising method for adaptive equidistant template iterative mean filtering that can be used for digital image processing in the fields of aerospace, industry and agriculture, medicine, astronomy and the like. Background technique [0002] In the process of scene imaging, spatial sampling and quantization, the image is often disturbed by various external noises, which degrades the image quality. Image noise is the most direct, harmful and critical problem affecting human observation. In order to reduce the influence of noise as much as possible, the noise-contaminated image must be denoised. The rule followed by image denoising is to protect as much detail information as possible, such as image edges, while removing noise, so that the image can more realistically reproduce the target scene. Although the traditional median filtering and mean filteri...

AI Technical Summary

Problems solved by technology

These improved algorithms improve the effect of removing salt and pepper noise, but generally speaking, there are several problems in these methods: one is that too many parameters are used in the algorithm, and the values ​​of some parameters are related to the image content, which is not conducive to the automatic processing of images ; Second, the calculation time is long, which cannot meet the real-time requirements of image processing; Third, for images with serious noise pollution, the denoising effect is poor, the quality of the restored image cannot be guaranteed, and it is easy to lose too many details in the original image

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