Abstract
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.
Abstract (translated)
低光照图像增强仍然是一项具有挑战性的任务，尤其是在极端照明退化情况下保持物体边缘连续性和精细结构细节方面。在本文中，我们提出了一种新型模型DRWKV（Detailed Receptance Weighted Key Value），该模型集成了我们提出的全局边缘Retinex（GER）理论，能够有效地将光照和边缘结构解耦以增强边缘保真度。其次，我们引入了进化WKV注意力机制，这是一种螺旋扫描机制，可以更有效地捕捉空间边缘连续性并建模不规则结构。第三，我们设计了双边光谱对齐器（Bi-SAB）以及定制的MS2-Loss来共同对准亮度和色度特征，从而提高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广泛实验表明，DRWKV在PSNR、SSIM和NIQE等指标上取得了领先性能，并且保持了较低的计算复杂度。此外，DRWKV还增强了下游任务中低光多目标跟踪的表现，验证了其泛化能力。
URL
https://arxiv.org/abs/2507.18594
