Model Study of Transient Imaging With Multi-Frequency Time-of-Flight Sensors - Wang, Hongman (IEEE transactions on pattern analysis and machine intelligence, 2021)
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