Images captured under low-light conditions often suffer from (partially) poor visibility. Besides unsatisfactory lightings, multiple types of degradation, such as noise and color distortion due to the limited quality of cameras, hide in the dark. In other words, solely turning up the brightness of dark regions will inevitably amplify pollution. Thus, low-light image enhancement should not only brighten dark regions, but also remove hidden artifacts. To achieve the goal, this work builds a simple yet effective network, which, inspired by Retinex theory, decomposes images into two components. Following a divide-and-conquer principle, one component (illumination) is responsible for light adjustment, while the other (reflectance) for degradation removal. In such a way, the original space is decoupled into two smaller subspaces, expecting for better regularization/learning. It is worth noticing that our network is trained with paired images shot under different exposure conditions, instead of using any ground-truth reflectance and illumination information. Extensive experiments are conducted to demonstrate the efficacy of our design and its superiority over the state-of-the-art alternatives, especially in terms of the robustness against severe visual defects and the flexibility in adjusting light levels. Our code is made publicly available at https://github.com/zhangyhuaee/KinD_plus .

中文翻译：

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超越照亮低光图像

在弱光条件下拍摄的图像通常（部分）能见度低。除了令人不满意的照明之外，多种类型的退化，例如由于相机质量有限而导致的噪声和色彩失真，都隐藏在黑暗中。换句话说，仅仅把暗区的亮度调高，不可避免地会放大污染。因此，低光图像增强不仅应该使黑暗区域变亮，而且还应该去除隐藏的伪影。为了实现这一目标，这项工作构建了一个简单而有效的网络，该网络受 Retinex 理论的启发，将图像分解为两个组件。遵循分而治之的原则，一个组件（照明）负责调光，而另一个（反射）负责去除退化。这样，原始空间被解耦成两个更小的子空间，期待更好的正则化/学习。值得注意的是，我们的网络使用在不同曝光条件下拍摄的成对图像进行训练，而不是使用任何地面真实反射率和照明信息。进行了大量实验以证明我们设计的有效性及其优于最先进替代方案的优越性，尤其是在对严重视觉缺陷的鲁棒性和调节光照水平的灵活性方面。我们的代码在 https://github.com/zhangyhuaee/KinD_plus 上公开提供。进行了大量实验以证明我们设计的有效性及其优于最先进替代方案的优越性，尤其是在对严重视觉缺陷的鲁棒性和调节光照水平的灵活性方面。我们的代码在 https://github.com/zhangyhuaee/KinD_plus 上公开提供。进行了大量实验以证明我们设计的有效性及其优于最先进替代方案的优越性，尤其是在对严重视觉缺陷的鲁棒性和调节光照水平的灵活性方面。我们的代码在 https://github.com/zhangyhuaee/KinD_plus 上公开提供。