  Convolutional neural networks have turned into an illustrious tool for image
fusion and super-resolution. However, their excellent performance cannot work
without large fixed-paired datasets; and additionally, these high-demanded
ground truth data always cannot be obtained easily in fusion tasks. In this
study, we show that, the structures of generative networks capture a great deal
of image feature priors, and then these priors are sufficient to reconstruct
high-quality fused super-resolution result using only low-resolution inputs. By
this way, we propose a novel self-supervised dataset-free method for adaptive
infrared (IR) and visible (VIS) image super-resolution fusion named Deep
Retinex Fusion (DRF). The key idea of DRF is first generating component priors
which are disentangled from physical model using our designed generative
networks ZipperNet, LightingNet and AdjustingNet, then combining these priors
which captured by networks via adaptive fusion loss functions based on Retinex
theory, and finally reconstructing the super-resolution fusion results.
Furthermore, in order to verify the effectiveness of our reported DRF, both
qualitative and quantitative experiments via comparing with other
state-of-the-art methods are performed using different test sets. These results
prove that, comparing with large datasets trained methods, DRF which works
without any dataset achieves the best super-resolution fusion performance; and
more importantly, DRF can adaptively balance IR and VIS information and has
good noise immunity. DRF codes are open source available at
https://github.com/GuYuanjie/Deep-Retinex-fusion.
