Low-light images captured in the real world are inevitably corrupted by sensor noise. Such noise is spatially variant and highly dependent on the underlying pixel intensity, deviating from the oversimplified assumptions in conventional denoising. Existing light enhancement methods either overlook the important impact of real-world noise during enhancement, or treat noise removal as a separate pre- or post-processing step. We present Coordinated Enhancement for Real-world Low-light Noisy Images (CERL), that seamlessly integrates light enhancement and noise suppression parts into a unified and physics-grounded optimization framework. For the real low-light noise removal part, we customize a self-supervised denoising model that can easily be adapted without referring to clean ground-truth images. For the light enhancement part, we also improve the design of a state-of-the-art backbone. The two parts are then joint formulated into one principled plug-and-play optimization. Our approach is compared against state-of-the-art low-light enhancement methods both qualitatively and quantitatively. Besides standard benchmarks, we further collect and test on a new realistic low-light mobile photography dataset (RLMP), whose mobile-captured photos display heavier realistic noise than those taken by high-quality cameras. CERL consistently produces the most visually pleasing and artifact-free results across all experiments. Our RLMP dataset and codes are available at: https://github.com/VITA-Group/CERL.

中文翻译：

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CERL：具有真实噪声的光增强统一优化框架

在现实世界中捕获的低光图像不可避免地会受到传感器噪声的破坏。这种噪声在空间上是变化的，并且高度依赖于底层像素强度，偏离了传统去噪中过于简单的假设。现有的光增强方法要么在增强过程中忽略现实世界噪声的重要影响，要么将噪声去除视为单独的预处理或后处理步骤。我们提出了现实世界L的协调增强ow-light Noisy Images (CERL)，将光增强和噪声抑制部分无缝集成到统一的基于物理的优化框架中。对于真正的低光噪声去除部分，我们定制了一个自监督去噪模型，无需参考干净的真实图像即可轻松适应。对于光增强部分，我们还改进了最先进主干的设计。然后将这两个部分联合制定为一个原则上的即插即用优化。我们的方法在定性和定量上与最先进的低光增强方法进行了比较。除了标准基准之外，我们还进一步收集和测试了一个新的逼真的低光移动摄影数据集（RLMP），其移动拍摄的照片显示出比高质量相机拍摄的照片更重的逼真噪点。CERL 在所有实验中始终产生最令人愉悦且无伪影的结果。我们的 RLMP 数据集和代码可在以下位置获得：https://github.com/VITA-Group/CERL.