  Enhancing images in low-light scenes is a challenging but widely concerned
task in the computer vision. The mainstream learning-based methods mainly
acquire the enhanced model by learning the data distribution from the specific
scenes, causing poor adaptability (even failure) when meeting real-world
scenarios that have never been encountered before. The main obstacle lies in
the modeling conundrum from distribution discrepancy across different scenes.
To remedy this, we first explore relationships between diverse low-light scenes
based on statistical analysis, i.e., the network parameters of the encoder
trained in different data distributions are close. We introduce the bilevel
paradigm to model the above latent correspondence from the perspective of
hyperparameter optimization. A bilevel learning framework is constructed to
endow the scene-irrelevant generality of the encoder towards diverse scenes
(i.e., freezing the encoder in the adaptation and testing phases). Further, we
define a reinforced bilevel learning framework to provide a meta-initialization
for scene-specific decoder to further ameliorate visual quality. Moreover, to
improve the practicability, we establish a Retinex-induced architecture with
adaptive denoising and apply our built learning framework to acquire its
parameters by using two training losses including supervised and unsupervised
forms. Extensive experimental evaluations on multiple datasets verify our
adaptability and competitive performance against existing state-of-the-art
works. The code and datasets will be available at
https://github.com/vis-opt-group/BL.
