Novel noise image fusion method based on CS-CT-CHMM

Abstract

The invention relates to a novel noise image fusion method based on a cycle spinning (CS) technology and Contourlet domain context hidden Markov model (CS-CT-CHMM). First, a source image containing a certain level of Gauss white noise is de-noised by use of the CS-CT-CHMM, wherein the structure of the context is calculated based on neighborhood entropies of a father node, two nearest cousin nodes and a current node of a Contourlet decomposition coefficient; then, Shearlet transform is performed on the de-noised image, and a fusion rule is designed by adopting a weighted average fusion strategy for low-frequency sub-band coefficients and adopting an improved pulse-coupled neural network (IPCNN) model for high-frequency sub-band coefficients; and finally, reverse Shearlet transform is performed to obtain a fused image. The CS-CT-CHMM de-noising method can protect detailed information of the image and suppress the pseudo Gibbs effect while effectively removing noise, while the fusion method combining Shearlet transform and IPCNN is advantageous in terms of image contrast enhancement and the amount of information. Compared with the traditional fusion method, the quality of the fused image is greatly improved.

Description

technical field [0001] The present invention relates to a new noise image fusion method combining cyclic translation CS technology and Contourlet domain context hidden Markov model (CS-CT-CHMM), which is a fusion method in the field of digital image processing technology. It is widely used in processing, target recognition and other systems. Background technique [0002] Image fusion is the effective synthesis of two or more images of a specific scene acquired by two or more sensors at the same time or at different times to generate a more comprehensive, accurate and reliable description of the scene information process. Image fusion can not only make up for the lack of image information acquired by a single information source, improve image resolution, improve visual effects, and enhance recognizability, but also reduce redundant information to a certain extent by integrating useful complementary features of the original image, eliminating Image potential uncertainty and ...

AI Technical Summary

Problems solved by technology

[0004] Wavelet transform has the advantages of time-frequency localization and multi-resolution, and can achieve optimal nonlinear approximation to one-dimensional signals with "point singularity" characteristics, but each scale can only be decomposed into three directions (horizontal, vertical and diagonal), lack of sufficient direction selectivity, so it is difficult to make full use of the inherent geometric regularity of the image itself, and cannot accurately describe the edge texture features in the image

This limitation makes wavelet transform unsuitable for processing images rich in texture information. The denoising method based on wavelet transform will inevitably weaken the details of the image such as the outline while reducing noise, resulting in blurred and distorted images.

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