A Novel Saliency-Based Decomposition Strategy for Infrared and Visible Image Fusion

Abstract

The image decomposition strategy that extracts salient features from the source image is crucial for image fusion. To this end, we proposed a novel saliency-based decomposition strategy for infrared and visible image fusion. In particular, the latent low-rank representation (LatLRR) and rolling guidance filter (RGF) are together employed to process source images, which is called DLatLRR_RGF. In this method, the source images are first decomposed to salient components and base components based on LatLRR, and the salient components are filtered by RGF. Then, the final base components can be calculated by the difference between the source image and the processed salient components. The fusion rule based on the nuclear-norm and modified spatial frequency is used to fuse the salient components. The base components are fused by the l2-energy minimization model. Finally, the fused image can be obtained by the fused base components and saliency detail components. Multiple groups of experiments on different pairs of infrared and visible images demonstrate that, compared with other state-of-the-art fusion algorithms, our proposed method possesses superior fusion performance from subjective and objective perspectives.

FERFusion: A Fast and Efficient Recursive Neural Network for Infrared and Visible Image Fusion

“…Subsequently, these image patches are processed by sparse coding to derive sparse representation coefficients [ 15 , 16 , 17 ]. Low-rank representation (LRR) methods are also utilized to extract saliency features from source images, too [ 18 , 19 ]. After obtaining image features or representation coefficients, they will be reconstructed to produce the final fusion results according to delicately designed fusion rules.…”