Self-Reference Deep Adaptive Curve Estimation for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2308.08197v4
• Date: Sun, 10 Sep 2023 10:53:48 GMT
• Title: Self-Reference Deep Adaptive Curve Estimation for Low-Light Image Enhancement
• Authors: Jianyu Wen, Chenhao Wu, Tong Zhang, Yixuan Yu, Piotr Swierczynski
• Abstract summary: We propose a 2-stage low-light image enhancement method called Self-Reference Deep Adaptive Curve Estimation (Self-DACE) In the first stage, we present an intuitive, lightweight, fast, and unsupervised luminance enhancement algorithm. We also propose a new loss function with a simplified physical model designed to preserve natural images' color, structure, and fidelity.
• Score: 7.253235412867934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a 2-stage low-light image enhancement method called Self-Reference Deep Adaptive Curve Estimation (Self-DACE). In the first stage, we present an intuitive, lightweight, fast, and unsupervised luminance enhancement algorithm. The algorithm is based on a novel low-light enhancement curve that can be used to locally boost image brightness. We also propose a new loss function with a simplified physical model designed to preserve natural images' color, structure, and fidelity. We use a vanilla CNN to map each pixel through deep Adaptive Adjustment Curves (AAC) while preserving the local image structure. Secondly, we introduce the corresponding denoising scheme to remove the latent noise in the darkness. We approximately model the noise in the dark and deploy a Denoising-Net to estimate and remove the noise after the first stage. Exhaustive qualitative and quantitative analysis shows that our method outperforms existing state-of-the-art algorithms on multiple real-world datasets.

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