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.

翻译：低光照场景下的图像增强是计算机视觉中一项具有挑战性但受到广泛关注的任务。主流基于学习的方法主要通过从特定场景中学习数据分布来获取增强模型，这导致在遇到未曾见过的真实场景时适应性差（甚至失败）。主要障碍在于不同场景间分布差异带来的建模难题。为此，我们首先基于统计分析探索不同低光照场景之间的关系，即在不同数据分布下训练的编码器网络参数具有相近性。我们引入双层范式从超参数优化的角度对上述潜在对应关系进行建模。构建双层学习框架以赋予编码器面向多样场景的场景无关通用性（即在自适应和测试阶段冻结编码器）。进一步，我们定义增强型双层学习框架，为场景特定解码器提供元初始化以进一步改善视觉质量。此外，为提高实用性，我们建立具有自适应去噪功能的Retinex诱导架构，并应用所建学习框架通过监督和无监督两种训练损失获取其参数。在多个数据集上的广泛实验验证了我们的适应性及与现有最先进方法相比的竞争性能。代码和数据集将在https://github.com/vis-opt-group/BL公开。