Implicit Neural Representation for Cooperative Low-light Image Enhancement
This work addresses low-light image enhancement for computer vision applications, but it is incremental as it builds on existing methods with new modules and unsupervised training.
The paper tackles the problem of low-light image enhancement by addressing unpredictable brightness degradation, noise, and the gap between metric-favorable and visual-friendly results, proposing NeRCo, an implicit neural representation method that robustly recovers perceptual-friendly results in an unsupervised manner, achieving superior effectiveness in experiments.
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.