Wavelet transform and joint sparse representation-based infrared and visible light image fusion method

Abstract

The invention provides a wavelet transform and joint sparse representation-based infrared and invisible light image fusion method, and relates to the field of image fusion. The method comprises the following steps of: firstly, carrying out DWT transform on a source image, decomposing the source image into a low-frequency sub-band matrix coefficient and a high-frequency sub-band coefficient, decomposing the low-frequency sub-band coefficient into a matrix by using a sliding window strategy and learning a dictionary in allusion to the decomposed low-frequency sub-band matrix; secondly, respectively fusing the low-frequency sub-band coefficient and the high-frequency sub-band coefficient; and finally reconstructing a fused image through DWT inverse transform. According to the method, effective sparse representation can be carried out on outstanding detail features of source images, and multi-scale fusion can be carried out on detail information of the images, so that target information of infrared images and background information such as details, profiles and the like of invisible light images are well retained, the target identification ability is improved, and benefit is brought to subsequent processing systems to extract and use the information; and compared with the traditional wavelet transform-based fusion method and the existing joint sparse representation-based fusion method, method provided by the invention has advantages.

Description

technical field [0001] The invention relates to the field of image fusion, in particular to a method for fusion of infrared and visible light images. Background technique [0002] Image fusion is a technology that combines the imaging information of the same scene by multiple sensors or multiple imaging by a single sensor to obtain more comprehensive, accurate and reliable information. Infrared and visible light image fusion is an important field of image fusion. The active research direction is that the fusion of infrared and visible light images can make full use of the complementary information of the two to obtain more comprehensive and accurate images, which has been widely used in military and civilian fields such as military reconnaissance and security monitoring. [0003] In the field of infrared and visible light image fusion, the fusion method based on wavelet transform is a mainstream method. Among them, discrete wavelet transform (DWT) has the characteristics of ...

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Problems solved by technology

However, the fusion method based on joint sparse representation is based on dictionary learning and sparse representation under a certain threshold, so it cannot accurately describe detailed information such as texture and edges, which will lose some high-frequency information, resulting in a decrease in the clarity of the fusion result

[0005] As mentioned above, the fusion method based on wavelet transform can extract source image information in multiple scales and directions, but the sparsity of low-frequency sub-bands is not good, and its direct fusion is not conducive to the extraction and preservation of features; while the fusion method based on joint sparse representation Although the fusion method can finely fit the data by learning the dictionary, it cannot accurately represent some high-frequency information, that is, it cannot analyze the data in multiple scales and directions, so there is a certain data loss problem

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