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

A multi-sensor, image fusion technology, applied in image enhancement, image data processing, instruments, etc., can solve the problems of unclear pulse delivery mechanism and many undetermined coefficients.

Inactive Publication Date: 2013-09-11
ENG UNIV OF THE CHINESE PEOPLES ARMED POLICE FORCE
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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 pr...

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

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[0052] The method of the present invention adopts computer MATLAB software technology to realize automatic operation during practical application. In order to better understand the technical solution of the present invention, this embodiment selects two SAR images for fusion. see figure 1 In the figure, the 8-band SAR image is recorded as "8-band", the 3-band SAR image is recorded as "3-band", and the final fusion image is recorded as "F"; following the technical solution of the present invention, the The multi-sensor fusion method based on NSST domain IICM specifically includes the following steps:

[0053] Step 1, input 8-band SAR image and 3-band SAR image, use NSST transform (since NSST transform is an existing technology, it will not be described here) Directional decomposition, in which the multi-scale decomposition series is set to 3 levels, according to the resolution layer from "coarse" to "fine", the multi-directional decomposition series are 2, 3, 4 in turn, and t...

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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...

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Application Information

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IPC IPC(8): G06T5/00
Inventor 孔韦韦王炳和
Owner ENG UNIV OF THE CHINESE PEOPLES ARMED POLICE FORCE
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