  In this paper, we propose a physics-inspired contrastive learning paradigm
for low-light enhancement, called PIE. PIE primarily addresses three issues:
(i) To resolve the problem of existing learning-based methods often training a
LLE model with strict pixel-correspondence image pairs, we eliminate the need
for pixel-correspondence paired training data and instead train with unpaired
images. (ii) To address the disregard for negative samples and the inadequacy
of their generation in existing methods, we incorporate physics-inspired
contrastive learning for LLE and design the Bag of Curves (BoC) method to
generate more reasonable negative samples that closely adhere to the underlying
physical imaging principle. (iii) To overcome the reliance on semantic ground
truths in existing methods, we propose an unsupervised regional segmentation
module, ensuring regional brightness consistency while eliminating the
dependency on semantic ground truths. Overall, the proposed PIE can effectively
learn from unpaired positive/negative samples and smoothly realize non-semantic
regional enhancement, which is clearly different from existing LLE efforts.
Besides the novel architecture of PIE, we explore the gain of PIE on downstream
tasks such as semantic segmentation and face detection. Training on readily
available open data and extensive experiments demonstrate that our method
surpasses the state-of-the-art LLE models over six independent cross-scenes
datasets. PIE runs fast with reasonable GFLOPs in test time, making it easy to
use on mobile devices.
