Space-time united image sequence multi-scale geometric transformation denoising method

Abstract

The invention discloses a space-time united image sequence multi-scale geometric transformation denoising method. By that an image multi-scale geometric analysis is introduced into the image denoising process, and image sequence space-time relevant information is united, a statistical distribution model of an image multi-scale geometric transformation domain coefficient, the built image statistical model serves as priori knowledge, a Bayesian lest square estimation method is adopted to obtain an optimal estimation result of non-noise-pollution image signals, and the problems that the situation that an image detail coefficient and image noise are difficult to distinguish often occurs in the denoising process of a conventional wavelet domain are solved. Meanwhile, plenty of image samples which are the same to a to-be-processed image in scene are collected and image statistical distribution modeling is conducted on the image samples, the statistical distribution model of the image multi-scale geometric transformation domain coefficient directly reflects statistical distribution features of texture details of edges and surface of the to-be-processed image and provides reliable priori distribution knowledge for Bayesian lest square estimation of the image, and space-time information united image sequence non-noise image signal estimation is achieved. A denoising effect of the image is improved, at the same time, image details are kept to a great extent.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a time-space joint image sequence multi-scale geometric transformation denoising method. Background technique [0002] Intelligent vision plays an increasingly important role in modern industrial manufacturing and safety monitoring. Automatic analysis and intelligent monitoring of target objects and natural scenes in the shooting field of view through computer image processing technology is an innovation of traditional industrial process automation and safety monitoring concepts. Effective processing of visual images and accurate extraction of image visual features are the keys to the success of intelligent visual surveillance systems. However, the image acquisition environment of industrial sites is often relatively harsh, such as uneven illumination, dusty site, heavy water mist, and electromagnetic interference from other equipment. Image signals are inevitably distu...

AI Technical Summary

Problems solved by technology

[0006] The present invention provides a time-space joint image sequence multi-scale geometric transformation denoising method, the purpose of which is to effectively overcome the inability to distinguish image edges and surface texture details and image noise in the prior art by combining image time-space related information. The problems of false boundaries and surface singularities can improve the effect of image denoising while maintaining image details to a great extent.

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