用物理模型提升单光子成像的视频重建质量
Quanta Diffusion
- 将物理成像模型融入扩散算法，动态建模光照与运动
- 在极低光下实现2.4 dB的PSNR提升
- 适合单光子图像传感器和超低光视频重建场景
我们提出Quanta Diffusion（QuDi），一种针对单光子成像的强健生成式视频重建方法。QuDi支持最新的量子图像传感器（QIS）和单光子雪崩二极管（SPADs），适用于极端低光成像条件。相比现有方法，QuDi克服了同时处理运动模糊与强散粒噪声的难题。其核心创新在于将基于物理的前向成像模型注入扩散算法，同时保持运动估计在闭环中。实验表明，QuDi在平均性能上比最优现有方法提升2.4 dB PSNR。
原文摘要 · Abstract (English)
We present Quanta Diffusion (QuDi), a powerful generative video reconstruction method for single-photon imaging. QuDi is an algorithm supporting the latest Quanta Image Sensors (QIS) and Single Photon Avalanche Diodes (SPADs) for extremely low-light imaging conditions. Compared to existing methods, QuDi overcomes the difficulties of simultaneously managing the motion and the strong shot noise. The core innovation of QuDi is to inject a physics-based forward model into the diffusion algorithm, while keeping the motion estimation in the loop. QuDi demonstrates an average of 2.4 dB PSNR improvement over the best existing methods.
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