A Two-Stage Exposure Controllable Network for Image Enhancement With Low-Light Degradation

Controllable low-light enhancement is beneficial to both image aesthetics and high-level vision tasks. Existing methods fail to generate smooth brightness transitions with low cost. In this paper, we propose an unsupervised two-stage exposure controllable network (TSECN) featuring low-cost and natural brightness customization. Conditioning on a color-correlation based exposure reference map (CERM), an offline naturalness-preserving exposure controller (ONEC) is implemented by modulating a latent scene illumination representation, which is predicted via a two-stage progressive enhancement pipeline (TPE). Specifically, the first stage performs unsupervised low-light denoising and improves visibility with deep gamma correction. The latter handles fine-grained quality refinement and representation learning. Extensive evaluations demonstrate the superior performance of our method across eight real-world datasets. On three paired datasets, TSECN outperforms advanced methods by an average PSNR improvement of 2.29 dB. Experiments on two high-level vision tasks also validate its value in application-oriented scenarios. The code will be available at https://github.com/cherrysherryplus/TSECN.