Multi-sensor image fusion method on basis of IICM (improved intersecting cortical model) in NSST (nonsubsampled shearlet transform) domain

Abstract

The invention discloses a multi-sensor image fusion method on the basis of an IICM (improved intersecting cortical model) in an NSST (nonsubsampled shearlet transform) domain. The multi-sensor image fusion method includes: step 1, building the IICM; step 2, inputting all the multi-sensor source images to be fused to perform NSST and acquiring one low-frequency sub-band image and multiple high-frequency sub-band images from each multi-sensor source image to be fused; step 3, importing all the high-frequency sub-band images and the low-frequency sub-band images into the IICM to complete fusion of the low-frequency sub-band images and fusion of the high-frequency sub-band images; step 4, performing NSST inverse transformation on the high-frequency sub-band images and the low-frequency sub-band images which are fused finally to acquire the final fusion image. The improved model, namely the IICM is provided on the basis of the classical ICM, multi-resolution analysis of the multi-sensor source images and reconstruction of the finally fused image are completed by means of NSST, and problems about fusion of the multi-sensor images are solved reasonably since the IICM is utilized for realizing fusions of the high-frequency sub-band images and the low-frequency sub-band images.

Description

technical field [0001] The invention belongs to the technical field of image information fusion, and in particular relates to a multi-sensor image fusion method based on NSST domain IICM. Background technique [0002] Image fusion refers to the integration of image target information about the same scene acquired by different sensors or the same sensor in different working modes, making full use of the complementary information contained in each source image to obtain a more comprehensive and more informative image. Reliable fused images. The rapid development of image sensor technology provides people with increasingly rich multi-sensor image data. However, as the opposite, these large amounts of multi-angle and multi-level image data are not only complementary, but also full of great redundancy and complexity. Therefore, how to efficiently extract more reliable, more refined, and more accurate information from these complementary and redundant multi-source massive data h...

AI Technical Summary

Problems solved by technology

In addition, the intersecting visual cortex model (ICM), as an improved model of the classic PCNN, has a good biological background and image processing capabilities, however, there are still many undetermined coefficients in the classic ICM model, and the pulse emission mechanism is not clear

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