URFusion: Unsupervised Unified Degradation-Robust Image Fusion Network.

Abstract

When dealing with low-quality source images, existing image fusion methods either fail to handle degradations or are restricted to specific degradations. This study proposes an unsupervised unified degradation-robust image fusion network, termed as URFusion, in which various types of degradations can be uniformly eliminated during the fusion process, leading to high-quality fused images. URFusion is composed of three core modules: intrinsic content extraction, intrinsic content fusion, and appearance representation learning and assignment. It first extracts degradation-free intrinsic content features from images affected by various degradations. These content features then provide feature-level rather than image-level fusion constraints for optimizing the fusion network, effectively eliminating degradation residues and reliance on ground truth. Finally, URFusion learns the appearance representation of images and assigns the statistical appearance representation of high-quality images to the content-fused result, producing the final high-quality fused image. Extensive experiments on multi-exposure image fusion and multi-modal image fusion tasks demonstrate the advantages of URFusion in fusion performance and suppression of multiple types of degradations. The code is available at https://github.com/hanna-xu/URFusion.