HSVFormer: Robust and Unsupervised HSV-based Transformer Framework for Low-Light Image Enhancement

Abstract

The following three factors restrict the application of existing low-light image enhancement methods: corruptions induced by the light-up process, color distortion, and a restricted generalization capacity due to limited paired training data. To address these limitations, we first combine HSV theory and Transformer, proposing a robust unsupervised low-light image enhancement framework, named HSVFormer. Secondly, we introduce brightness disturbance and design an unsupervised value enhancement network, which estimates brightness information and restores degraded brightness information to obtain enhanced reflectance. Finally, we utilize the V-subspace and devise a value-guided multi-head channel self-attention to capture brightness representations of regions with different brightness conditions and guide the modeling of non-local interactions. Experiment results on publicly available datasets demonstrate that HSVFormer can achieve superior performance compared with state-of-the-art approaches. The code is available at https://github.com/m0fig/HSVFormer.