Images captured in low-light environment often suffer from complex degradation. Simply adjusting light would inevitably result in burst of hidden noise and color distortion. To seek results with satisfied lighting, cleanliness, and realism from degraded inputs, this paper presents a novel framework inspired by the divide-and-rule principle, greatly alleviating the degradation entanglement. Assuming that an image can be decomposed into texture (with possible noise) and color components, one can specifically execute noise removal and color correction along with light adjustment. Towards this purpose, we propose to convert an image from the RGB space into a luminance-chrominance one. An adjustable noise suppression network is designed to eliminate noise in the brightened luminance, having the illumination map estimated to indicate noise boosting levels. The enhanced luminance further serves as guidance for the chrominance mapper to generate realistic colors. Extensive experiments are conducted to reveal the effectiveness of our design, and demonstrate its superiority over state-of-the-art alternatives both quantitatively and qualitatively on several benchmark datasets. Our code is publicly available at https://github.com/mingcv/Bread.

翻译：在低光环境中捕捉到的图像往往受到复杂的降解。 简单的调整光将不可避免地导致隐藏的噪音和色彩扭曲的爆发。 为了从退化的投入中寻求满意的光亮、清洁性和现实性的结果,本文件提出了一个由分而治之原则启发的新框架,大大减轻了降解的纠缠。假设图像可以分解成质地(可能有噪音)和颜色成分,人们可以具体地进行噪音清除和色彩校正,同时对光线进行调整。为此目的,我们提议将一个RGB空间的图像转换成一个发亮的色调之一。一个可调整的噪音抑制网络的设计是为了消除光亮的光亮点中的噪音,用光化图估计显示噪音的振动水平。增强的光度进一步作为色调绘图器的指南,以产生现实的颜色。我们进行了广泛的实验,以揭示我们的设计的有效性,并表明它优于若干基准数据集上的最新替代物的定量和定性。我们的代码可在 https://githbub.com/ming/Bread 上公开查阅。