A novel fusion scheme for visible and infrared images based on compressive sensing

An appropriate fusion of infrared and visible images can integrate their complementary information and obtain more reliable and better description of the environmental conditions. Compressed sensing theory, as a low signal sampling and compression method based on the sparsity of signal under a certain transformation, is widely used in various fields. Applying to the image fusion applications, only a part of sparse coefficients are needed to be fused. Furthermore, the fused sparse coefficients can be used to accurately reconstruct the fused image. The CS-based fusion approach can greatly reduce the computational complexity and simultaneously enhance the quality of the fused image. In this paper, an improved image fusion scheme based on compressive sensing is presented. This proposed approach can preserve more detail information, such as edges, lines and contours in comparison to the conventional transform-based image fusion approaches. In the proposed approach, the sparse coefficients of the source images are obtained by discrete wavelet transform. The low and high coefficients of infrared and visible images are fused by an improved entropy weighted fusion rule and a max-abs-based fusion rule, respectively. The fused image is reconstructed by a compressive sampling matched pursuit algorithm after local linear projection using a random Gaussian matrix. Several comparative experiments are conducted. The experimental results show that the proposed image fusion scheme can achieve better image fusion quality than the existing state-of-the-art methods.