A Lightweight Image Entropy-Based Divide-and-Conquer Network for Low-Light Image Enhancement
Hongjun Wu, Haoran Qi, Jingzhou Luo, Yining Li, Zhi Juan Jin · 2022 IEEE International Conference on Multimedia and Expo (ICME) · 2022
Images captured in low-light conditions usually suffer from degradation problems. Based on the observation, we found that different image regions have different enhancement difficulties and can be processed by networks with different capacities. Hence, in this work, we propose a lightweight image entropy-based divide-and-conquer network called IEDCN for low-light image enhancement. Our network consists of Pre-processing, Enhancement, and Refinement three stages. In the Pre-processing Stage, we crop the low-light image into patches, and classify them into “simple”, “medium” and “hard” groups according to their image entropy. Then patches in each group are enhanced separately by corresponding branches with the divide-and-conquer strategy in the Enhancement Stage. Finally, the combined segments from the branches are refined by the last stage as the final output. Compared with other state-of-the-art methods, our IEDCN with only 0.73M parameters can effectively improve the quality of enhanced images, while saving up to 53% Flops on the LOL dataset.