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Infrared and visible images, as multi-modal image pairs, show significantdifferences in the expression of the same scene. The image fusion task is facedwith two problems: one is to maintain the unique features between differentmodalities, and the other is to maintain features at various levels like localand global features. This paper discusses the limitations of deep learningmodels in image fusion and the corresponding optimization strategies. Based onartificially designed structures and constraints, we divide models intoexplicit models, and implicit models that adaptively learn high-level featuresor can establish global pixel associations. Ten models for comparisonexperiments on 21 test sets were screened. The qualitative and quantitativeresults show that the implicit models have more comprehensive ability to learnimage features. At the same time, the stability of them needs to be improved.Aiming at the advantages and limitations to be solved by existing algorithms,we discuss the main problems of multi-modal image fusion and future researchdirections.

Explicit and implicit models in infrared and visible image fusion