BiEvLight：面向低光照图像增强的任务感知事件精炼双层学习
BiEvLight: Bi-level Learning of Task-Aware Event Refinement for Low-Light Image Enhancement
将事件去噪建模为受增强任务约束的双层优化问题，使去噪网络学习到面向下游增强的事件表征，这一思路可迁移到其他多模态融合任务中，避免静态预处理带来的过/欠去噪权衡。
原文依据 we recast it as a bilevel optimization problem constrained by the enhancement task
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论事件相机凭借其高动态范围，在低光照图像增强（LLIE）中展现出巨大潜力。现有工作主要聚焦于设计有效的模态融合策略。然而，一个关键挑战在于事件中固有的背景活动（BA）噪声与图像中低信噪比（SNR）造成的双重退化，这导致模态融合过程中出现严重的噪声耦合，形成关键的性能瓶颈。因此，我们认为精确的事件去噪是释放基于事件的融合全部潜力的前提。为此，我们提出BiEvLight，一个分层且任务感知的框架，通过利用增强与去噪之间内在的相互依赖关系来协同优化二者。具体而言，BiEvLight利用图像与事件之间强梯度相关性，构建梯度引导的事件去噪先验，以缓解重噪声区域去噪不足的问题。此外，我们没有将事件去噪视为静态预处理阶段——这不可避免地会在过度去噪与去噪不足之间产生权衡，且无法适应特定增强目标的需求——而是将其重新定义为受增强任务约束的双层优化问题。通过跨任务交互，上层去噪问题学习到为下层增强目标量身定制的事件表征，从而显著提升整体增强质量。在真实噪声数据集SED上的大量实验表明，我们的方法显著优于最优方法，在峰值信噪比（PSNR）、PSNR*和SSIM上分别平均提升1.30dB、2.03dB和0.047。代码将公开于https://github.com/iijjlk/BiEvlight。
英文原文摘要 1776 字符
Event cameras, with their high dynamic range, show great promise for Low-light Image Enhancement (LLIE). Existing works primarily focus on designing effective modal fusion strategies. However, a key challenge is the dual degradation from intrinsic background activity (BA) noise in events and low signal-to-noise ratio (SNR) in images, which causes severe noise coupling during modal fusion, creating a critical performance bottleneck. We therefore posit that precise event denoising is the prerequisite to unlocking the full potential of event-based fusion. To this end, we propose BiEvLight, a hierarchical and task-aware framework that collaboratively optimizes enhancement and denoising by exploiting their intrinsic interdependence. Specifically, BiEvLight exploits the strong gradient correlation between images and events to build a gradient-guided event denoising prior that alleviates insufficient denoising in heavily noisy regions. Moreover, instead of treating event denoising as a static pre-processing stage--which inevitably incurs a trade-off between over- and under-denoising and cannot adapt to the requirements of a specific enhancement objective--we recast it as a bilevel optimization problem constrained by the enhancement task. Through cross-task interaction, the upper-level denoising problem learns event representations tailored to the lower-level enhancement objective, thereby substantially improving overall enhancement quality. Extensive experiments on the Real-world noise Dataset SED demonstrate that our method significantly outperforms state-of-the-art (SOTA) approaches, with average improvements of 1.30dB in PSNR , 2.03dB in PSNR* and 0.047 in SSIM, respectively. The code will be publicly available at https://github.com/iijjlk/BiEvlight.