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The following three factors restrict the application of existing low-lightimage enhancement methods: unpredictable brightness degradation and noise,inherent gap between metric-favorable and visual-friendly versions, and thelimited paired training data. To address these limitations, we propose animplicit Neural Representation method for Cooperative low-light imageenhancement, dubbed NeRCo. It robustly recovers perceptual-friendly results inan unsupervised manner. Concretely, NeRCo unifies the diverse degradationfactors of real-world scenes with a controllable fitting function, leading tobetter robustness. In addition, for the output results, we introducesemantic-orientated supervision with priors from the pre-trainedvision-language model. Instead of merely following reference images, itencourages results to meet subjective expectations, finding morevisual-friendly solutions. Further, to ease the reliance on paired data andreduce solution space, we develop a dual-closed-loop constrained enhancementmodule. It is trained cooperatively with other affiliated modules in aself-supervised manner. Finally, extensive experiments demonstrate therobustness and superior effectiveness of our proposed NeRCo. Our code isavailable at https://github.com/Ysz2022/NeRCo.

Implicit Neural Representation for Cooperative Low-light Image Enhancement