The non-line-of-sight imaging technique aims to reconstruct targets from multiply reflected light. For most existing methods, dense points on the relay surface are raster scanned to obtain high-quality reconstructions, which requires a long acquisition time. In this work, we propose a signal-surface collaborative regularization (SSCR) framework that provides noise-robust reconstructions with a minimal number of measurements. Using Bayesian inference, we design joint regularizations of the estimated signal, the$3D$voxel-based representation of the objects, and the 2D surface-based description of the targets. To our best knowledge, this is the first work that combines regularizations in mixed dimensions for hidden targets. Experiments on synthetic and experimental datasets illustrated the efficiency of the proposed method under both confocal and non-confocal settings. We report the reconstruction of the hidden targets with complex geometric structures with only 5 × 5 confocal measurements from public datasets, indicating an acceleration of the conventional measurement process by a factor of 10,000. Besides, the proposed method enjoys low time and memory complexity with sparse measurements. Our approach has great potential in real-time non-line-of-sight imaging applications such as rescue operations and autonomous driving.

Non-line-of-sight (NLOS) imaging aims at reconstructing the location, shape, albedo, and surface normal of the hidden object around the corner with measured transient data. Due to its strong potential in various fields, it has drawn much attention in recent years. However, long exposure time is not always available for applications such as auto-driving, which hinders the practical use of NLOS imaging. Although scanning fewer points can reduce the total measurement time, it also brings the problem of imaging quality degradation. This paper proposes a general learning-based pipeline for increasing imaging quality with only a few scanning points. We tailor a neural network to learn the operator that recovers a high spatial resolution signal. Experiments on synthetic and measured data indicate that the proposed method provides faithful reconstructions of the hidden scene under both confocal and non-confocal settings. Compared with original measurements, the acquisition of our approach is 16 times faster while maintaining similar reconstruction quality. Besides, the proposed pipeline can be applied directly to existing optical systems and imaging algorithms as a plug-in-and-play module. We believe the proposed pipeline is powerful in increasing the frame rate in NLOS video imaging.

In this paper a new algorithm for digital video coding is presented that is suitable for digital storage and video transmission applications in the range of 5 to 10 Mbps. The scheme is based on frame differencing and, unlike recent proposals, does not employ motion estimation and compensation. A novel adaptive grouping structure is used to segment the video sequence into groups of frames of variable sizes. Within each group, the frame difference is taken in a closed loop Differential Pulse Code Modulation (DPCM) structure and then decomposed into different frequency subbands. The important subbands are transformed using the Discrete Cosine Transform (DCT) and the resulting coefficients are adaptively quantized and runlength coded. The adaptation is based on the variance of sample values in each subband. To reduce the computation load, a very simple and efficient way has been used to estimate the variance of the subbands. It is shown that for many types of sequences, the performance of the proposed coder is comparable to that of coding methods which use motion parameters.