A Dataset-free Self-supervised Disentangled Learning Method for Adaptive Infrared and Visible Images Super-resolution Fusion

This study proposes a novel general dataset-free self-supervised learning framework based-on physical model named self-supervised disentangled learning (SDL), and proposes a novel method named Deep Retinex fusion (DRF) which applies SDL framework with generative networks and Retinex theory in infrared and visible images super-resolution fusion. Meanwhile, a generative dual-path fusion network ZipperNet and adaptive fusion loss function Retinex loss are designed for effectively high-quality fusion. The core idea of DRF (based-on SDL) consists of two parts: one is generating components which are disentangled from physical model using generative networks; the other is loss functions which are designed based-on physical relation, and generated components are combined by loss functions in training phase. Furthermore, in order to verify the effectiveness of our proposed DRF, qualitative and quantitative comparisons compared with six state-of-the-art methods are performed on three different infrared and visible datasets. Our code will be open source available soon at https://github.com/GuYuanjie/Deep-Retinex-fusion.