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Recently, edge-preserving filters have achieved great success in infrared (IR) and visible (VI) image fusion field. However, most edge-preserving filters are complex. In this paper, with the side window filtering technology by which most filters can improve their edge-preserving capabilities, we propose a general perceptual IR and VI image fusion framework with simple linear filter. Firstly, the source images are decomposed into edge feature components, hybrid components and base components by using linear filter and its side window version. Then, these components are combined by max-absolute fusion rule and improved max-absolute fusion rule. Finally, the fused image is reconstructed by adding all the fused components. In our experiments, two popular linear filters, i.e., box filter and Gaussian filter, are used to verify the effectiveness of the proposed framework. Experimental results show that the proposed fusion framework can obtain better perceptual fusion results than compared methods. INDEX TERMS Image fusion, side window filtering, linear filter, infrared images, visible images.

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Among the key sub-topics of image fusion, infrared-visible image fusion technology is widely used in modern military and civilian domain. Driven by the data-free and end-toend advancedness, growing research efforts have been devoted to convert the image fusion task into a norm minimization problem. To simultaneously keep the highlighted thermal information in the infrared image and the clear appearance information in the visible image, we propose a lowcomplexity fusion algorithm via guided transformation minimization, namely L2GTM. In particular, we formulate the fusion task as a solely 2 norm minimization problem, which enjoys the uniqueness of the optimal solution. To adaptively integrate information from both infrared and visible images, we introduce guided weights to determine the degree of information retention. Experiments on public datasets validate the competitiveness of L2GTM from both the visual quality and objective evaluation perspective.

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