用可控扩散模型从事件流重建图像，效果优于传统方法。
eBIRD: Event-based Intensity Image Reconstruction Using Controllable Diffusion Models
- 结合DDPM与ControlNet，用事件流引导图像生成。
- 在N-MNIST上达PSNR 23.34dB，RGBE-Gaze上达PSNR 19.08dB。
- 通用与专用模型各适配不同场景，适合事件相机图像重建研究者。
由于事件数据具有二值性、稀疏性和异步性，从事件流重建强度图像仍具挑战。本文提出eBIRD，一种基于事件引导的重建框架，结合DDPM与基于ControlNet的条件控制。我们分析了在手写数字（N-MNIST）和人脸（RGBE-Gaze）重建中，通用与专用扩散学习策略的表现，使用33ms事件窗口。在N-MNIST上，通用模型取得最佳效果（MSE 0.0052，SSIM 0.8982，PSNR 23.34dB）；在RGBE-Gaze上，专用模型表现更优（MSE 0.0161，SSIM 0.7605，PSNR 19.08dB）。初步结果表明，可控扩散模型是事件引导图像重建的有前景方向，且最优学习策略依赖于具体重建任务领域。
原文摘要 · Abstract (English)
Intensity-image reconstruction from event streams remains a challenging problem due to the binary, sparse, and asynchronous nature of event data. This work proposes eBIRD, an event-guided reconstruction framework that combines a DDPM with ControlNet-based conditioning. We analyze generic and specialized diffusion learning strategies for handwritten digit (N-MNIST) and face (RGBE-Gaze) reconstruction using 33ms event windows. On N-MNIST, the general model achieves the best reconstruction quality (MSE 0.0052, SSIM 0.8982, PSNR 23.34dB), whereas the specialized model performs best on RGBE-Gaze (MSE 0.0161, SSIM 0.7605, PSNR 19.08dB). These preliminary results suggest that controllable diffusion models are a promising approach for event-guided intensity-image reconstruction, while highlighting that the preferred learning strategy depends on the reconstruction domain.
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