看见不可见的：利用事件相机在黑暗中放大
Seeing the Unseen: Zooming in the Dark with Event Cameras
AI总结：
RetinexEVSR通过事件驱动和Retinex先验知识提升低光视频超分辨率性能，实现高对比度细节恢复与低光伪影抑制。
摘要：
本文针对低光视频超分辨率（LVSR）问题，旨在从低光、低分辨率（LR）输入中恢复高质量视频。现有LVSR方法往往难以恢复细节，因为对比度有限且高频信息不足。为克服这些挑战，我们提出了RetinexEVSR，这是首个基于事件的LVSR框架，利用高对比度事件信号和受Retinex启发的先验知识，以在低光场景下提升视频质量。与以往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，以从噪声事件数据和退化RGB帧中提取并整合有意义的线索。具体而言，设计了一个光照引导的事件增强模块，利用Retinex模型生成的光照图逐步细化事件特征，从而抑制低光伪影的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增强模块，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，我们的方法相比以往基于事件的方法，在信噪比上达到2.95 dB的提升，同时将运行时间减少了65%。代码：https://github.com/DachunKai/RetinexEVSR。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
