Abstract:
Current infrared and visible images fusion algorithms couldn't efficiently extract the object information in the infrared image while retaining the background information in visible image. To solve this problem, a new infrared and visible image fusion algorithm by low rank representation and dictionary learning was proposed to promote contrast and preserve edges for the source images. Firstly, low rank decomposition was performed on the input images to obtain their corresponding low rank and sparse components which could well represent the sparse feature of images. Secondly, the sparse representation using OMP algorithm with a trained dictionary was adapted to calculate the low rank coefficient and sparse coefficient, then by adding the common low rank sparse coefficient to the maximum norm of uncommon sparse coefficients could retain the background information of the source image effectively. Finally, we reconstructed the fused image from the fused sparse coefficients and adaptive dictionary. Experimental results demonstrated that this fusion algorithm could highlight the infrared objects and retained the images detailed information as well as retain the background information in visible image.