This paper addresses low-light video super-resolution (LVSR), aiming to restore high-resolution videos from low-light, low-resolution (LR) inputs. Existing LVSR methods often struggle to recover fine details due to limited contrast and insufficient high-frequency information. To overcome these challenges, we present RetinexEVSR, the first event-driven LVSR framework that leverages high-contrast event signals and Retinex-inspired priors to enhance video quality under low-light scenarios. Unlike previous approaches that directly fuse degraded signals, RetinexEVSR introduces a novel bidirectional cross-modal fusion strategy to extract and integrate meaningful cues from noisy event data and degraded RGB frames. Specifically, an illumination-guided event enhancement module is designed to progressively refine event features using illumination maps derived from the Retinex model, thereby suppressing low-light artifacts while preserving high-contrast details. Furthermore, we propose an event-guided reflectance enhancement module that utilizes the enhanced event features to dynamically recover reflectance details via a multi-scale fusion mechanism. Experimental results show that our RetinexEVSR achieves state-of-the-art performance on three datasets. Notably, on the SDSD benchmark, our method can get up to 2.95 dB gain while reducing runtime by 65% compared to prior event-based methods. Code: https://github.com/DachunKai/RetinexEVSR.

翻译：本文针对低光照视频超分辨率（LVSR）问题，旨在从低光照、低分辨率（LR）输入中恢复高分辨率视频。现有LVSR方法由于对比度有限和高频信息不足，往往难以恢复精细细节。为克服这些挑战，我们提出了RetinexEVSR，这是首个利用高对比度事件信号和受Retinex启发的先验知识来增强低光照场景下视频质量的事件驱动LVSR框架。与先前直接融合退化信号的方法不同，RetinexEVSR引入了一种新颖的双向跨模态融合策略，从含噪事件数据和退化的RGB帧中提取并整合有意义的线索。具体而言，我们设计了一个光照引导的事件增强模块，利用从Retinex模型导出的光照图逐步细化事件特征，从而抑制低光照伪影同时保留高对比度细节。此外，我们提出了一个事件引导的反射率增强模块，该模块利用增强的事件特征通过多尺度融合机制动态恢复反射率细节。实验结果表明，我们的RetinexEVSR在三个数据集上实现了最先进的性能。值得注意的是，在SDSD基准测试中，与先前基于事件的方法相比，我们的方法可获得高达2.95 dB的性能增益，同时减少65%的运行时间。代码：https://github.com/DachunKai/RetinexEVSR。