用有序事件流学习光照残差，提升暗光图像增强效果
EvLIR: Learning Illumination Residuals from Ordered Events for Low-Light Image Enhancement
- 基于事件流的时间残差建模，捕捉短时动态变化
- 在四个基准上平均达25.63dB PSNR，11项最优
- 适合需要高动态范围与低光增强的视觉系统
暗光图像增强在输入帧缺失结构、过曝噪声和弱局部对比度时极为困难。事件相机提供高时间分辨率的亮度变化异步观测，但以往方法常将体素通道视为无序或静态特征堆叠，未显式建模窗口内的时间演化，削弱了事件的时间信息优势。本文提出EvLIR，一种基于有序事件的时序残差增强框架，通过保留事件流的时间分段顺序，并引入轻量级ConvGRU的时序事件残差模块（TERM）编码短窗事件动态。生成的时序状态转化为有界光照修正，为Retinex风格的光照估计提供空间自适应的光度引导，并支持后续可靠性感知的图像-事件恢复。在SDE和SDSD室内外基准上，EvLIR在十二个数据集-指标组合中取得十一项最佳结果，四个基准平均PSNR达25.63~28.30 dB，SSIM为0.827。
原文摘要 · Abstract (English)
Low-light image enhancement is severely ill-posed when the input frame contains missing structure, saturated noise, and weak local contrast. Event cameras provide asynchronous brightness-change observations with high temporal resolution, but prior works often treat voxel channels as an unordered or static feature stack before fusion, rather than explicitly modeling their within-window temporal evolution, weakening the temporal evidence that makes events useful. We propose EvLIR, a temporal-residual enhancement framework that learns illumination residuals from ordered events for low-light image enhancement. Given a low-light frame and its aligned event voxel, EvLIR preserves the ordered temporal bins of the event stream and introduces a Temporal Event Residual Module (TERM) to encode short-window event dynamics with a lightweight ConvGRU. The resulting temporal state is converted into a bounded illumination correction, which provides spatially adaptive photometric guidance for Retinex-style illumination estimation and subsequent reliability-aware image-event restoration. On SDE and SDSD indoor/outdoor benchmarks, EvLIR achieves the best result on eleven of twelve dataset-metric pairs, with average scores of 25.63~dB PSNR, 28.30~dB PSNR*, and 0.827 SSIM across the four benchmarks.
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