Infrared-visible image fusion method based on sparse and prior joint saliency detection and LatLRR-FPDE.

In this paper, a new infrared and visible image fusion method based on saliency detection and LatLRRFPDE is proposed to ensure the saliency of the fused target and improve the clarity of the texture detail information of the latent low-rank representation (LatLRR) fusion method. In the feature representation stage, source images are decomposed into low-rank and sparse parts based on LatLRR. Then Fourth order Partial Differential Equation (FPDE) is used to extract high-frequency detail information from the lowrank part. In the fusion decision stage, according to the characteristic difference of the decomposed sub-bands, a multi-scale fusion scheme is carried out using rules such as EM (expectation-maximization), average (AVG), and the maximum absolute value. To guarantee the integrity of thermal radiation features in the target area in fusion results, an infrared image saliency detection algorithm is proposed, which utilizes the fusion information obtained from the sparse and prior saliency feature. Specifically, the algorithm uses convex hull center prior and contrast prior to obtain the background-suppressed saliency map. Meanwhile, to make full use of the high-dimensional saliency features in the image decomposition stage, the prior saliency map and the sparse saliency map are fused to improve the accuracy of saliency detection without the demand to extract redundant feature, which largely increases time-efficiency of the model. Finally, infrared feature compensation is performed on the target region of the fused image based on the saliency mapping strategy to make the final fused image having a better infrared target feature representation. The experiments are based on registered public datasets and compared with various popular image fusion algorithms. The subjective and objective results show that the proposed algorithm can effectively keep the integrity of salient infrared features and has superior visual quality.(c) 2023 Elsevier Inc. All rights reserved.