Unspervised Low-Light Inage Enhancement Based On Deep Lightness And Grey Pixel Estimation

Images captured under low-light conditions often suffer from inadequate lightness and low color contrast, resulting in reduced performance of computer vision-related applications. In this work, we propose an unsupervised network for enhancing low-light images, which enhances the lightness and color constancy of images. To achieve this, we introduce two sub-modules, LC-Net and GP-Net, which estimate high-order curves for lightness enhancement and gray pixels for color constancy, respectively. The proposed method shows promising results in improving the quality of low-light images. To further enhance the information reasoning capability of our proposed method, we adopt an attention block that combines channel and space attention as a basic unit for the network. We evaluate the performance of our method quantitatively and visually in comparison to other existing low-light image enhancement techniques through experiments, the results indicate our method can outperform other approaches greatly in terms of color fidelity and lightness enhancement.