Low-light situations severely restrict the pursuit of aesthetic quality in consumer photography. Although many efforts are devoted to designing heuristics, it is generally mired in a shallow spiral of tedium, such as piling up complex network architectures and empirical strategies. How to delve into the essential physical principles of illumination compensation has been neglected. Following the way of simplifying the complexity, this paper innovatively proposes a simple and efficient Noise-Aware Illumination Interpolator (NAI$_2$). According to the constraint principle of illuminance and reflectance within a limited dynamic range, as a prior knowledge in the recovery process, we construct a learnable illuminance interpolator and thereby compensating for non-uniform lighting. With the intention of adapting denoising without annotated data, we design a self-calibrated denoiser with the intrinsic image properties to acquire noiseless low-light images. Starting from the properties of natural image manifolds, a self-regularized recovery loss is introduced as a way to encourage more natural and realistic reflectance map. The model architecture and training losses, guided by prior knowledge, complement and benefit each other, forming a powerful unsupervised leaning framework. Comprehensive experiments demonstrate that the proposed algorithm produces competitive qualitative and quantitative results while maintaining favorable generalization capability in unknown real-world scenarios. The code will be available online upon publication of the paper.

翻译：低光照条件严重制约了消费摄影中美感质量的追求。尽管已有大量研究专注于启发式设计，但通常陷入复杂网络架构堆砌与经验策略的浅层循环困境，对光照补偿本质物理原理的深入探索却被忽视。遵循化繁为简的思路，本文创新性地提出一种简洁高效的噪声感知照度插值器（NAI$_2$）。基于有限动态范围内照度与反射率的约束原理，我们将该先验知识融入复原过程，构建可学习的照度插值器以实现非均匀光照补偿。为适应无标注数据下的去噪任务，我们利用图像内在属性设计自校准去噪器，获取无噪声低光照图像。从自然图像流形特性出发，引入自正则化复原损失函数以生成更自然真实的反射率图。由先验知识引导的模型架构与训练损失相互补充、彼此增益，形成强大的无监督学习框架。综合实验表明，所提算法在未知真实场景中保持优异泛化能力的同时，能够产生具有竞争力的定性与定量结果。论文发表后将公开代码。