Low-light image enhancement remains a challenging task, particularly in preserving object edge continuity and fine structural details under extreme illumination degradation. In this paper, we propose a novel model, DRWKV (Detailed Receptance Weighted Key Value), which integrates our proposed Global Edge Retinex (GER) theory, enabling effective decoupling of illumination and edge structures for enhanced edge fidelity. Secondly, we introduce Evolving WKV Attention, a spiral-scanning mechanism that captures spatial edge continuity and models irregular structures more effectively. Thirdly, we design the Bilateral Spectrum Aligner (Bi-SAB) and a tailored MS2-Loss to jointly align luminance and chrominance features, improving visual naturalness and mitigating artifacts. Extensive experiments on five LLIE benchmarks demonstrate that DRWKV achieves leading performance in PSNR, SSIM, and NIQE while maintaining low computational complexity. Furthermore, DRWKV enhances downstream performance in low-light multi-object tracking tasks, validating its generalization capabilities.

翻译：低光照图像增强仍是一项具有挑战性的任务，尤其是在极端光照退化条件下保持物体边缘连续性与精细结构细节方面。本文提出了一种新颖的模型DRWKV（Detailed Receptance Weighted Key Value），该模型融合了我们提出的全局边缘Retinex（GER）理论，能够有效解耦光照与边缘结构，从而提升边缘保真度。其次，我们引入了演化WKV注意力机制，这是一种螺旋扫描机制，能够更有效地捕捉空间边缘连续性并建模不规则结构。第三，我们设计了双边频谱对齐器（Bi-SAB）与定制的MS2损失函数，共同对齐亮度与色度特征，以提升视觉自然度并减少伪影。在五个低光照图像增强基准数据集上的大量实验表明，DRWKV在PSNR、SSIM和NIQE指标上均取得领先性能，同时保持了较低的计算复杂度。此外，DRWKV在低光照多目标跟踪任务中提升了下游性能，验证了其泛化能力。