Bilevel Fast Scene Adaptation for Low-Light Image Enhancement

TL;DR

This paper introduces a bilevel learning framework for low-light image enhancement that improves adaptability across diverse scenes by leveraging statistical analysis and meta-initialization, achieving state-of-the-art results.

Contribution

The paper proposes a novel bilevel learning approach with a scene-irrelevant encoder and scene-specific decoder, enhancing low-light image enhancement adaptability and performance.

Findings

Outperforms existing methods on multiple datasets

Demonstrates high adaptability to unseen scenes

Achieves superior visual quality in low-light conditions

Abstract

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.