DPEC: Dual-Path Error Compensation Method for Enhanced Low-Light Image Clarity

For the task of low-light image enhancement, deep learning-based algorithms have demonstrated superiority and effectiveness compared to traditional methods. Existing deep learning algorithms are proposed mainly based on the Retinex theory but overlook the noise and color distortion present in the input, which frequently results in significant noise amplification and local color distortion in the final results. To address this, we propose a Dual-Path Error Compensation method (DPEC), which aims to improve image quality in low-light conditions. DPEC performs precise pixel-level error estimation, which accurately captures subtle pixels differences, and independent denoising, which effectively removes unnecessary noise. This method restores image brightness while preserving local texture details and avoiding noise amplification. Furthermore, to compensate for the traditional CNN's limited ability to capture long-range semantic information and considering both computational speed and resource efficiency, we integrated the VMamba architecture into the backbone of DPEC. In addition, we introduced the HIS-Retinex loss to constrain the training of DPEC, ensuring that the overall brightness distribution of the images more closely aligns with real-world conditions. Comprehensive quantitative and qualitative experimental results demonstrate that our algorithm significantly outperforms state-of-the-art methods across six benchmark tests.