TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2110.02477v1
• Date: Wed, 6 Oct 2021 03:20:18 GMT
• Title: TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement
• Authors: Xinxu Wei, Xianshi Zhang, Shisen Wang, Yanlin Huang, and Yongjie Li
• Abstract summary: We propose a Two-Stage Network with Channel Attention (denoted as TSN-CA) to enhance the brightness of the low-light image. We conduct extensive experiments to demonstrate that our method achieves excellent effect on brightness enhancement as well as denoising, details preservation and halo artifacts elimination.
• Score: 11.738203047278848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light image enhancement is a challenging low-level computer vision task because after we enhance the brightness of the image, we have to deal with amplified noise, color distortion, detail loss, blurred edges, shadow blocks and halo artifacts. In this paper, we propose a Two-Stage Network with Channel Attention (denoted as TSN-CA) to enhance the brightness of the low-light image and restore the enhanced images from various kinds of degradation. In the first stage, we enhance the brightness of the low-light image in HSV space and use the information of H and S channels to help the recovery of details in V channel. In the second stage, we integrate Channel Attention (CA) mechanism into the skip connection of U-Net in order to restore the brightness-enhanced image from severe kinds of degradation in RGB space. We train and evaluate the performance of our proposed model on the LOL real-world and synthetic datasets. In addition, we test our model on several other commonly used datasets without Ground-Truth. We conduct extensive experiments to demonstrate that our method achieves excellent effect on brightness enhancement as well as denoising, details preservation and halo artifacts elimination. Our method outperforms many other state-of-the-art methods qualitatively and quantitatively.

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