Infrared and visible image fusion method based on sparse representation

Abstract

The invention provides an infrared and visible image fusion method based on sparse representation. The fusion method includes the steps that images are chunked, a dictionary is trained, sparse coefficients of the two source images are respectively solved, the sparse coefficient of a fused image is obtained through a module value maximax return rule, and the fusion result image is obtained through reconstruction. The fusion method can adapt to the own characteristics of the infrared and visible images, and compared with a traditional method, the extracted representation coefficients of the source images are better in sparsity and characteristic retentivity and can reflect substantive characteristics and internal structures of signals better, so that the fusion effect of the infrared and visible images is effectively improved.

Description

technical field [0001] The invention relates to the field of image fusion. Background technique [0002] The image fusion of infrared and visible light is an important part of the field of image fusion. This technology has broad application prospects in medical imaging, remote sensing imaging, machine vision, security monitoring and other fields. [0003] At present, the fusion methods of infrared and visible light images mainly include: methods based on digital weighting, methods based on pyramid decomposition, methods based on wavelet transform, and methods based on Ridgelet, Curvelet, and Contourlet. Multiscale geometric analysis methods such as transformations. [0004] The method based on digital weighting directly divides the source image through weighting or large and small operations to obtain the fusion image, which has the advantages of simple method and fast speed, but the contrast and signal-to-noise ratio of the fusion result are low, which cannot highlight the...

AI Technical Summary

Problems solved by technology

[0005] As mentioned earlier, the traditional infrared and visible light image fusion method is not an optimal image representation model for complex and diverse image signals because the basis function is fixed.

However, in the new image model based on sparse representation, there is a certain information loss problem due to the use of the mean value processing.

Images