Deep Symmetric Network for Underexposed Image Enhancement with Recurrent Attentional Learning
Lin Zhao, Shao-Ping Lu, Tao Chen, Zhenglu Yang, Ariel Shamir
Abstract
Underexposed image enhancement is of importance in many research domains. In this paper, we take this problem as image feature transformation between the underexposed image and its paired enhanced version, and we propose a deep symmetric network for the issue. Our symmetric network adapts invertible neural networks (INN) for bidirectional feature learning between images, and to ensure the mutual propagation invertible we specifically construct two pairs of encoder-decoder with the same pretrained parameters. This invertible mechanism with bidirectional feature transformations enable us to both avoid colour bias and recover the content effectively for image enhancement. In addition, we propose a new recurrent residual-attention module (RRAM), where the recurrent learning network is designed to gradually perform the desired colour adjustments. Ablation experiments are executed to show the role of each component of our new architecture. We conduct a large number of experiments on two datasets to demonstrate that our method achieves the state-of-the-art effect in underexposed image enhancement. Code is available at https://www.shaopinglu.net/proj-iccv21/ImageEnhancement.html.