The following three factors restrict the application of existing low-light image enhancement methods: unpredictable brightness degradation and noise, inherent gap between metric-favorable and visual-friendly versions, and the limited paired training data. To address these limitations, we propose an implicit Neural Representation method for Cooperative low-light image enhancement, dubbed NeRCo. It robustly recovers perceptual-friendly results in an unsupervised manner. Concretely, NeRCo unifies the diverse degradation factors of real-world scenes with a controllable fitting function, leading to better robustness. In addition, for the output results, we introduce semantic-orientated supervision with priors from the pre-trained vision-language model. Instead of merely following reference images, it encourages results to meet subjective expectations, finding more visual-friendly solutions. Further, to ease the reliance on paired data and reduce solution space, we develop a dual-closed-loop constrained enhancement module. It is trained cooperatively with other affiliated modules in a self-supervised manner. Finally, extensive experiments demonstrate the robustness and superior effectiveness of our proposed NeRCo. Our code is available at https://github.com/Ysz2022/NeRCo.

翻译：以下三个因素限制了现有低光图像增强方法的应用：不可预测的亮度退化与噪声、度量友好版本与视觉友好版本之间的固有差距，以及有限的成对训练数据。为应对这些限制，我们提出了一种用于协同低光图像增强的隐式神经表示方法，命名为NeRCo。该方法以无监督方式鲁棒地恢复出感知友好的结果。具体而言，NeRCo通过可控拟合函数统一真实场景中的多样退化因素，从而提升鲁棒性。此外，针对输出结果，我们引入基于预训练视觉语言模型先验的语义导向监督。该方法不仅限于遵循参考图像，而是鼓励结果满足主观期望，寻找更视觉友好的解决方案。进一步地，为减轻对成对数据的依赖并缩小解空间，我们开发了一种双闭环约束增强模块，并以自监督方式与其他附属模块协同训练。最后，大量实验证明了所提NeRCo的鲁棒性和优越有效性。我们的代码已在https://github.com/Ysz2022/NeRCo公开。