用事件相机提升低光双曝光成像，减少运动模糊和图像失真。
Dual-Exposure Imaging with Events
- 利用事件数据校准双曝光图像的动态信息，实现精准对齐。
- 在多个数据集上优于现有方法，显著降低运动伪影。
- 适合需要高动态范围低光成像的自动驾驶与机器人场景。
通过结合短曝光与长曝光图像的优势，双曝光成像（DEI）可提升低光环境下的图像质量。然而，现有方法因场景运动导致的空间位移以及不同曝光时间引起的图像特征差异，不可避免地产生伪影。为此，本文提出一种基于事件的双曝光成像（E-DEI）算法，利用事件相机的高时间分辨率，从双曝光图像对与事件中重建高质量图像，提供精确的帧间/帧内动态信息。具体而言，将该复杂任务分解为事件驱动的运动去模糊与低光图像增强两个子任务，并设计了双路径并行特征传播架构。提出双路径特征对齐与融合（DFAF）模块，借助事件辅助对齐并融合来自双曝光图像的特征。此外，构建了真实世界数据集Paired low-/normal-light Images and Events（PIED）。多数据集实验表明，本方法具有显著优势。代码与数据集已公开于GitHub。
原文摘要 · Abstract (English)
By combining complementary benefits of short- and long-exposure images, Dual-Exposure Imaging (DEI) enhances image quality in low-light scenarios. However, existing DEI approaches inevitably suffer from producing artifacts due to spatial displacement from scene motion and image feature discrepancies from different exposure times. To tackle this problem, we propose a novel Event-based DEI (E-DEI) algorithm, which reconstructs high-quality images from dual-exposure image pairs and events, leveraging high temporal resolution of event cameras to provide accurate inter-/intra-frame dynamic information. Specifically, we decompose this complex task into an integration of two sub-tasks, i.e., event-based motion deblurring and low-light image enhancement tasks, which guides us to design E-DEI network as a dual-path parallel feature propagation architecture. We propose a Dual-path Feature Alignment and Fusion (DFAF) module to effectively align and fuse features extracted from dual-exposure images with assistance of events. Furthermore, we build a real-world Dataset containing Paired low-/normal-light Images and Events (PIED). Experiments on multiple datasets show the superiority of our method. The code and dataset are available at github.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。