Local statistical feature-based adaptive gradient threshold anisotropic filtering method

Abstract

The invention belongs to the image processing field and relates to an adaptive anisotropic filtering method. With the method adopted, a gradient threshold value can be changed according to local statistical features, and in anisotropic diffusion, the weights of different pixels are dynamically adjusted, so that the denoising ability of anisotropic filtering in a high-intensity noise environment can be improved. According to the local statistical feature-based adaptive gradient threshold anisotropic filtering method provided by the technical schemes of the invention, based on an anisotropic filtering algorithm, a gradient operator is utilized to identify image gradient change caused by noises and image gradient change caused by edges, neighborhood weighted averaging is adopted to remove small gradient change caused by noises and retain large gradient change caused by edges, and the above process is carried out iteratively until noises in an image are removed. The method of the invention is mainly applied to image processing.

Description

technical field [0001] The invention relates to the field of image processing, in particular to the problem of selection of gradient threshold in anisotropic filter image denoising. Specifically, it involves an adaptive gradient threshold anisotropic filtering method based on local statistical features. Background technique [0002] Images are an important way to identify external information, and the clarity of images has a great impact on human cognition and analysis of the outside world. In the process of image acquisition and transmission, it will be more or less disturbed by external noise, and the basic information of some images will be weakened or eliminated by these noises, resulting in the reduction of image quality. Therefore, the noise in the image can be processed by a series of methods such as smoothing and filtering to improve the image quality. [0003] There are currently several mainstream image denoising methods: Gaussian filtering, mean filtering, media...

AI Technical Summary

Problems solved by technology

Compared with some traditional spatial filtering techniques, the advantage of anisotropic diffusion is that it can retain or even enhance the edge information in the image while removing the noise. At the same time, the noise similar to the edge point may also be enhanced, especially in the In a high-intensity noise environment, this effect is even greater. Therefore, the current mainstream anisotropic filtering method often has a very large de-noising ability when denoising high-intensity noise. Therefore, for different noise points, the edge Different anisotropic filtering methods for points and smooth pixels are current research hotspots

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