Wang Hongman, Qiao Hui, Lin Jingyu, Wu Rihui, Liu Yebin, Dai Qionghai
IEEE Trans Pattern Anal Mach Intell. 2021 Oct;43(10):3523-3539. doi: 10.1109/TPAMI.2020.2981574. Epub 2021 Sep 2.
As an emerging imaging modality, transient imaging that records the transient information of light transport has significantly shaped our understanding of scenes. In spite of the great progress made in computer vision and optical imaging fields, commonly used multi-frequency time-of-flight (ToF) sensors are still afflicted with the band-limited modulation frequency and long acquisition process. To overcome such barriers, more effective image-formation schemes and reconstruction algorithms are highly desired. In this paper, we propose a compressive transient imaging model, without any priori knowledge, by constructing a near-tight-frame based representation of the ToF imaging principle. We prove that the compressibility of sensor measurements can be presented in the Fourier domain and held in the frame, and the ToF measurements possess multi-scale characteristics. Solving the inverse problems in transient imaging with our proposed model consists of two major steps, including a compressed-sensing-based approach for full measurement recovery, which essentially reduces the capture time, and a wavelet-based transient image reconstruction framework, which realizes adaptive transient image reconstruction and achieves highly accurate reconstruction results. The compressive transient imaging model is suitable for various existing multi-frequency ToF sensors and requires no hardware modifications. Experimental results using synthetic and real online datasets demonstrate its promising performance.
作为一种新兴的成像模态，记录光传输瞬态信息的瞬态成像显著地塑造了我们对场景的理解。尽管在计算机视觉和光学成像领域取得了巨大进展，但常用的多频飞行时间（ToF）传感器仍然受到带宽限制的调制频率和长时间采集过程的困扰。为了克服这些障碍，非常需要更有效的成像方案和重建算法。在本文中，我们通过构建基于近紧框架的ToF成像原理表示，提出了一种无需任何先验知识的压缩瞬态成像模型。我们证明了传感器测量的可压缩性可以在傅里叶域中呈现并保存在框架中，并且ToF测量具有多尺度特征。用我们提出的模型解决瞬态成像中的逆问题包括两个主要步骤，包括一种基于压缩感知的全测量恢复方法，该方法本质上减少了捕获时间，以及一个基于小波的瞬态图像重建框架，该框架实现了自适应瞬态图像重建并取得了高精度的重建结果。压缩瞬态成像模型适用于各种现有的多频ToF传感器，并且无需硬件修改。使用合成和真实在线数据集的实验结果证明了其有前景的性能。
