Lit the Darkness: Three-Stage Zero-Shot Learning for Low-Light Enhancement with Multi-Neighbor Enhancement Factors

Abstract

Low-light images represent an obstacle for computer vision tasks due to the lack of perceptual quality. Also, it is challenging to enhance images and adapt to different illumination conditions. To address this problem, we introduce a zero-shot learning approach. We use a 3-stage model trained without the need for paired or unpaired images to improve the lighting and texture of images. The first stage extracts an enhancement pixel-wise map using depth-wise separable convolution. It also tries to extract enhancement factors so that it can consider results e.g., neighbors from other steps in the following stage. The second stage is a recurrent network that enhances the image iteratively while keeping a small model size. The third stage represents an unsupervised network to preserve semantic information and benefit from it during training. We show extensive experiments on benchmark datasets to compare our model with previous state-of-the-art models quantitatively and qualitatively.