Papers › DPEC: Dual-Path Error Compensation Method for Enhanced Low-Light Image Clarity
DPEC: Dual-Path Error Compensation Method for Enhanced Low-Light Image Clarity
Shuang Wang, Qianwen Lu, Boxing Peng, Yihe Nie, Qingchuan Tao
For the task of low-light image enhancement, deep learning-based algorithms have demonstrated superiority and effectiveness compared to traditional methods. However, these methods, primarily based on Retinex theory, tend to overlook the noise and color distortions in input images, leading to significant noise amplification and local color distortions in enhanced results. To address these issues, we propose the Dual-Path Error Compensation (DPEC) method, designed to improve image quality under low-light conditions by preserving local texture details while restoring global image brightness without amplifying noise. DPEC incorporates precise pixel-level error estimation to capture subtle differences and an independent denoising mechanism to prevent noise amplification. We introduce the HIS-Retinex loss to guide DPEC's training, ensuring the brightness distribution of enhanced images closely aligns with real-world conditions. To balance computational speed and resource efficiency while training DPEC for a comprehensive understanding of the global context, we integrated the VMamba architecture into its backbone. Comprehensive quantitative and qualitative experimental results demonstrate that our algorithm significantly outperforms state-of-the-art methods in low-light image enhancement. The code is publicly available online at https://github.com/wangshuang233/DPEC.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Low-Light Image Enhancement | LOL | DPEC_ | Average PSNR | 27.01 | #11 of 40 | Archive leaderboard | report |
| Low-Light Image Enhancement | LOL | DPEC_ | SSIM | 0.872 | #11 of 40 | Archive leaderboard | report |
| Low-Light Image Enhancement | LOL | DPEC | Average PSNR | 24.80 | #20 of 40 | Archive leaderboard | report |
| Low-Light Image Enhancement | LOL | DPEC | SSIM | 0.855 | #20 of 40 | Archive leaderboard | report |
| Low-Light Image Enhancement | LOLv2-synthetic | DPEC_ | Average PSNR | 29.95 | #1 of 9 | Archive leaderboard | report |
| Low-Light Image Enhancement | LOLv2-synthetic | DPEC_ | SSIM | 0.950 | #1 of 9 | Archive leaderboard | report |
| Low-Light Image Enhancement | LOLv2-synthetic | DPEC | Average PSNR | 26.19 | #8 of 9 | Archive leaderboard | report |
| Low-Light Image Enhancement | LOLv2-synthetic | DPEC | SSIM | 0.939 | #8 of 9 | Archive leaderboard | report |
| Low-Light Image Enhancement | LSRW | DPEC | Average PSNR | 19.643 | #1 of 1 | Archive leaderboard | report |
| Low-Light Image Enhancement | LSRW | DPEC | SSIM | 0.576 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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