FDEFusion: End-to-End Infrared and Visible Image Fusion Method Based on Frequency Decomposition and Enhancement

In the image fusion field, fusing infrared images (IRIs) and visible images (VIs) excelled is a key area. The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image. Accordingly, efficiently combining the advantages of both images while overcoming their shortcomings is necessary. To handle this challenge, we developed an end-to-end IRI and VI fusion method based on frequency decomposition and enhancement. By applying concepts from frequency domain analysis, we used the layering mechanism to better capture the salient thermal targets from the IRIs and the rich textural information from the VIs, respectively, significantly boosting the image fusion quality and effectiveness. In addition, the backbone network combined Restormer Blocks and Dense Blocks; Restormer blocks utilize global attention to extract shallow features. Meanwhile, Dense Blocks ensure the integration between shallow and deep features, thereby avoiding the loss of shallow attributes. Extensive experiments on TNO and MSRS datasets demonstrated that the suggested method achieved state-of-the-art (SOTA) performance in various metrics: Entropy (EN), Mutual Information (MI), Standard Deviation (SD), The Structural Similarity Index Measure (SSIM), Fusion quality (Qabf), MI of the pixel ( $FM{I}_{pixel}$), and modified Visual Information Fidelity ( $VI{F}_m$).