Image fusion method based on Riesz-Lap transformation and PCNN

Abstract

The invention discloses an image fusion method based on Riesz-Lap transformation and PCNN, Riesz transformation has the capability of separating low-level features of an image, and important information perceived by human vision also comes from the low-level features of the image. According to the invention, a to-be-fused source image is decomposed into a Riesz transformation domain; the fractional order Laplace operator and multi-harmonic spline wavelet transform are performed on the image sequence of the Riesz transform domain to enable image representation to have direction characteristicsand multi-resolution characteristics, and the fusion is carried out by combining global coupling characteristics of a pulse coupling neural network on the basis of Riesz Laplace wavelets. Through multi-focus, medical CT and nuclear magnetic resonance, remote sensing panchromatic and multispectral image various application scene tests, the obtained image fusion result is good in visual effect, thecontrast ratio and definition are improved, and the operation efficiency is high.

Description

technical field [0001] The invention relates to the field of computer applications, in particular to an image fusion method based on Riesz-Lap transformation and PCNN. Background technique [0002] In order to obtain an image of a scene to be studied, due to the different focus positions during imaging, the imaging clarity of different areas of the scene image is also different. In order to obtain a clearer image of the scene and more information, this requires The method of image fusion is used to solve this kind of multi-focus problem. In addition to multi-focus fusion, there are a large number of other image fusion problems to be solved. With the advancement of technology, there are more and more types of imaging sensors, and different sensors have different capabilities to obtain different information. For example, remote sensing satellite imaging equipment acquisition Multi-band remote sensing images, panchromatic images and multispectral images, among which panchromat...

AI Technical Summary

Problems solved by technology

The method based on wavelet decomposition performs well in local frequency analysis, and reduces the correlation between subband coefficients compared with pyramid decomposition; the contourlet transform, one of the super wavelets, has multi-directionality and can better express nature than wavelet transform Singular lines and planes in the scene; Non-subsampled Contourlet Transform (NSCT) uses sub-band separation to avoid spectral aliasing after restoration caused by the up-down sampling process, but actually increases the amount of data and calculation

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