Self-adaptive weighted Gaussian curvature filtering method based on image edge indicator function

Abstract

The invention provides a self-adaptive weighted Gaussian curvature filtering method based on an image edge indicator function, which is used for improving a Gaussian curvature filtering algorithm in a curvature filtering algorithm and constructing a self-adaptive fractional order-integer order energy functional and a local weighted projection operator by adopting the edge indicator function. Iterative updating of a weighted projection operator is adaptively and accurately controlled by minimizing the energy function, and the texture details of the image are well kept while the denoising performance is improved. According to the method, the fractional order and the weight of each item in the energy functional are adjusted in a self-adaptive mode, iterative updating of a projection operator can be accurately controlled, and incomplete image denoising or excessive smoothing is effectively prevented.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to an adaptive weighted Gaussian curvature filtering method based on an image edge indicator function. Background technique [0002] Noise often appears as isolated pixels or pixel blocks that cause strong visual effects on images, which directly affects subsequent processing tasks such as image analysis and understanding. The purpose of image denoising is to reduce the noise in the image while keeping the edge texture details of the image from being destroyed as much as possible, but denoising and edge preservation are often a contradictory process. How to remove noise and preserve details Finding a better balance point is the focus of image denoising research. [0003] The current image denoising methods mainly include methods based on deep learning, transform domain, sparse dictionary, non-local self-similar prior, and partial differential equations. The image ...

AI Technical Summary

Problems solved by technology

The above algorithm can achieve fast blind image denoising without knowing the noise intensity, but it does not distinguish between noise points and edge pixels, so that noise points and edge pixels affect each other. However, this type of algorithm has the problem of incomplete denoising or excessively smoothed edges

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