  The visibility of real-world images is often limited by both low-light and
low-resolution, however, these issues are only addressed in the literature
through Low-Light Enhancement (LLE) and Super- Resolution (SR) methods.
Admittedly, a simple cascade of these approaches cannot work harmoniously to
cope well with the highly ill-posed problem for simultaneously enhancing
visibility and resolution. In this paper, we propose a normalizing flow
network, dubbed LoLiSRFLow, specifically designed to consider the degradation
mechanism inherent in joint LLE and SR. To break the bonds of the one-to-many
mapping for low-light low-resolution images to normal-light high-resolution
images, LoLiSRFLow directly learns the conditional probability distribution
over a variety of feasible solutions for high-resolution well-exposed images.
Specifically, a multi-resolution parallel transformer acts as a conditional
encoder that extracts the Retinex-induced resolution-and-illumination invariant
map as the previous one. And the invertible network maps the distribution of
usually exposed high-resolution images to a latent distribution. The backward
inference is equivalent to introducing an additional constrained loss for the
normal training route, thus enabling the manifold of the natural exposure of
the high-resolution image to be immaculately depicted. We also propose a
synthetic dataset modeling the realistic low-light low-resolution degradation,
named DFSR-LLE, containing 7100 low-resolution dark-light/high-resolution
normal sharp pairs. Quantitative and qualitative experimental results
demonstrate the effectiveness of our method on both the proposed synthetic and
real datasets.
