Image interfusion method based non-subsampling contourlet transform

Abstract

The present invention proposes an image fusion method based on non-sub-sampling contourlet transform, including the following steps: ① using non-sub-sampling contourlet transform to perform multi-scale decomposition on images to obtain high-frequency sub-image series and low-frequency sub-images; ② high-frequency sub-images For the low-frequency sub-image, use the fusion method based on the definition selection; ③For the low-frequency sub-image, use the segmentation condition to judge, and select the four-region correlation or two-region correlation method to segment the low-frequency sub-image according to the judgment result. The region is fused using the corresponding pixel gray value selection rule; ④ The fusion result of the high-frequency sub-image and the fusion result of the low-frequency sub-image are inversely transformed by non-subsampling contourlet to obtain the final fusion result. The invention can effectively retain the edge information in the source image, increase the contrast of the target background, highlight the feature of the image target, and the fusion result is suitable for further processing such as visual analysis and target detection.

Description

technical field [0001] The invention relates to the field of image fusion, in particular to an image fusion method based on Nonsubsampled Contourlet Transform (NSCT). Background technique [0002] The so-called image fusion refers to combining the information of two or more source images to obtain a more accurate, comprehensive and reliable image description of the same scene. Image fusion makes full use of redundant information and complementary information contained in multiple images to be fused. The fused image should be more in line with the visual characteristics of humans or machines, so as to facilitate further image analysis, target detection, recognition or tracking. [0003] Image fusion is also divided into three levels from low to high: pixel-level fusion, feature-level fusion and decision-level fusion. Pixel-level fusion is to directly perform pixel-associated fusion processing under the condition of strict image registration. Feature-level fusion is based o...

AI Technical Summary

Problems solved by technology

This type of method has the characteristics of simple structure and easy implementation, but the description of the target is often not achieved by a single pixel or pixels in a single window area, so the pixel-based fusion method has certain limitations for highlighting the target.

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