Bayesian Fusion for Infrared and Visible Images

• URL: http://arxiv.org/abs/2005.05839v1
• Date: Tue, 12 May 2020 14:57:19 GMT
• Title: Bayesian Fusion for Infrared and Visible Images
• Authors: Zixiang Zhao, Shuang Xu, Chunxia Zhang, Junmin Liu, Jiangshe Zhang
• Abstract summary: In this paper, a novel Bayesian fusion model is established for infrared and visible images. We aim at making the fused image satisfy human visual system. Compared with the previous methods, the novel model can generate better fused images with high-light targets and rich texture details.
• Score: 26.64101343489016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared and visible image fusion has been a hot issue in image fusion. In this task, a fused image containing both the gradient and detailed texture information of visible images as well as the thermal radiation and highlighting targets of infrared images is expected to be obtained. In this paper, a novel Bayesian fusion model is established for infrared and visible images. In our model, the image fusion task is cast into a regression problem. To measure the variable uncertainty, we formulate the model in a hierarchical Bayesian manner. Aiming at making the fused image satisfy human visual system, the model incorporates the total-variation(TV) penalty. Subsequently, the model is efficiently inferred by the expectation-maximization(EM) algorithm. We test our algorithm on TNO and NIR image fusion datasets with several state-of-the-art approaches. Compared with the previous methods, the novel model can generate better fused images with high-light targets and rich texture details, which can improve the reliability of the target automatic detection and recognition system.

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