Injected Infrared and Visible Image Fusion via $L_{1}$ Decomposition Model and Guided Filtering

In this paper, an infrared (IR) and visible (VIS) image fusion algorithm is designed for the injection of the IR objects into the VIS background in a perceptual manner. It consists of four parts: image decomposition, layer fusion, image reconstruction, and image refinement. An edge-preserving filter is constructed for image decomposition, in which an $L_1$ regularization term and a fractional gradient are newly introduced. The resulting filter is capable of not only preserving edges, but also attenuating the influence of the IR background. A two-layer fusion rule is adopted, which consists of a routine weighted-average fusion rule and an injected fusion rule. It ensures that the fused image is with both rich background information of the VIS image and the salient features of the IR image. After image reconstruction, the guided filter is applied again to the IR image to refine the fused image, such that the final version of the fused image is with satisfactory human visual perception under even dim lights. The effectiveness and superiority of our fusion algorithm are illustrated by the results of ablation studies and comparative experiments.