DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion

Abstract

Infrared and visible image fusion, a hot topic in the field of image processing, aims at obtaining fused images keeping the advantages of source images. This paper proposes a novel auto-encoder (AE) based fusion network. The core idea is that the encoder decomposes an image into background and detail feature maps with low- and high-frequency information, respectively, and that the decoder recovers the original image. To this end, the loss function makes the background/detail feature maps of source images similar/dissimilar. In the test phase, background and detail feature maps are respectively merged via a fusion module, and the fused image is recovered by the decoder. Qualitative and quantitative results illustrate that our method can generate fusion images containing highlighted targets and abundant detail texture information with strong robustness and meanwhile surpass state-of-the-art (SOTA) approaches.

Cited by in corpus (7)

• CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion
• ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning
• MMSFormer: Multimodal Transformer for Material and Semantic Segmentation
• A Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting
• Deep Unfolding Multi-modal Image Fusion Network via Attribution Analysis
• RGBX-DiffusionDet: A Framework for Multi-Modal RGB-X Object Detection Using DiffusionDet
• SWIR-LightFusion: Multi-spectral Semantic Fusion of Synthetic SWIR with Thermal IR (LWIR/MWIR) and RGB