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.

翻译：针对低光照图像增强任务，基于深度学习的算法相较于传统方法已展现出优越性与有效性。然而，这些主要基于Retinex理论的方法往往忽视输入图像中的噪声与色彩失真，导致增强结果中出现显著的噪声放大与局部色彩畸变。为解决这些问题，我们提出了双路径误差补偿方法，该方法旨在通过保留局部纹理细节并在不放大噪声的情况下恢复图像全局亮度，以提升低光照条件下的图像质量。DPEC整合了精确的像素级误差估计以捕捉细微差异，并采用独立的去噪机制以防止噪声放大。我们引入了HIS-Retinex损失函数来指导DPEC的训练，确保增强图像的亮度分布与现实场景高度吻合。为在训练DPEC全面理解全局上下文的同时平衡计算速度与资源效率，我们将VMamba架构集成至其主干网络。综合定量与定性实验结果表明，我们的算法在低光照图像增强任务中显著优于现有先进方法。代码已公开于https://github.com/wangshuang233/DPEC。