Brightness Perceiving for Recursive Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2504.02362v1
• Date: Thu, 03 Apr 2025 07:53:33 GMT
• Title: Brightness Perceiving for Recursive Low-Light Image Enhancement
• Authors: Haodian Wang, Long Peng, Yuejin Sun, Zengyu Wan, Yang Wang, Yang Cao,
• Abstract summary: We propose a brightness-perceiving-based framework for high dynamic range low-light image enhancement.<n>Our framework consists of two parallel sub-networks: Adaptive Contrast and Texture enhancement network (ACT-Net) and Brightness Perception network (BP-Net)<n>Compared with eleven existing representative methods, the proposed method achieves new SOTA performance on six reference and no reference metrics.
• Score: 8.926230015423624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the wide dynamic range in real low-light scenes, there will be large differences in the degree of contrast degradation and detail blurring of captured images, making it difficult for existing end-to-end methods to enhance low-light images to normal exposure. To address the above issue, we decompose low-light image enhancement into a recursive enhancement task and propose a brightness-perceiving-based recursive enhancement framework for high dynamic range low-light image enhancement. Specifically, our recursive enhancement framework consists of two parallel sub-networks: Adaptive Contrast and Texture enhancement network (ACT-Net) and Brightness Perception network (BP-Net). The ACT-Net is proposed to adaptively enhance image contrast and details under the guidance of the brightness adjustment branch and gradient adjustment branch, which are proposed to perceive the degradation degree of contrast and details in low-light images. To adaptively enhance images captured under different brightness levels, BP-Net is proposed to control the recursive enhancement times of ACT-Net by exploring the image brightness distribution properties. Finally, in order to coordinate ACT-Net and BP-Net, we design a novel unsupervised training strategy to facilitate the training procedure. To further validate the effectiveness of the proposed method, we construct a new dataset with a broader brightness distribution by mixing three low-light datasets. Compared with eleven existing representative methods, the proposed method achieves new SOTA performance on six reference and no reference metrics. Specifically, the proposed method improves the PSNR by 0.9 dB compared to the existing SOTA method.

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