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.

Magnetoacoustic tomography with magnetic induction (MAT-MI) is a coupled-physics medical imaging modality for determining conductivity distribution in biological tissue. The capability of MAT-MI to provide high-resolution images has been demonstrated experimentally. MAT-MI involves two steps. The first step is a well-posed inverse source problem for acoustic wave equations, which has been well studied in the literature. This paper concerns mathematical analysis of the second step, a quantitative reconstruction of the conductivity from knowledge of the internal data recovered in the first step, using techniques such as time reversal. The problem is modeled by a system derived from Maxwell's equations. We show that a single internal data determines the conductivity. A global Lipschitz-type stability estimate is obtained. A numerical approach for recovering the conductivity is proposed, and results from computational experiments are presented.

With the increasing prevalence of online shopping, many companies have begun to provide "live help" functions, through instant messaging or text chatting, on their Web sites to facilitate interactions between online consumers and customer service representatives (CSRs). The continuing reliance of these functions on text-based communication limits nonverbal communication with consumers and the social contexts for the information conveyed. However, with the help of emerging multimedia technologies, companies can now use computer-generated voice and humanoid avatars to embody CSRs, thus enriching the interactive experiences of their customers. In this study, a laboratory experiment was conducted to empirically test the effects of text-to-speech (TTS) voice and 3-dimensional (3D) avatars on consumer trust toward CSRs. TTS voice was implemented to deliver answers aloud. A 3D avatar served as the humanoid representation of a CSR. The results demonstrated that the presence of TTS voice significantly increases consumers' cognitive and emotional trust toward the CSR. These findings offer practitioners guidelines to improve the interface design of real-time human-to-human communications for e-commerce Web sites.

Expansion and growth of online shopping has led many companies to provide real-time communications on their Web sites to facilitate human-to-human interaction between service representatives and customers. The current study analyzes the interface design of such Live Help functions. More specifically, it attempts to understand whether or not the implementation of Text-To-Speech (TTS) voice communication and 3D avatars in the user interface of Live Help affects consumers' views of their own interactions with a service person.A laboratory experiment was designed to empirically test the hypotheses that TTS voice communication and 3D avatars can significantly affect consumers' perceptions of presence and flow. A 2*3 full factorial design was used (assessing three options for the use of voice and text, and two options for the use of avatars). The results of the experiment demonstrate that the presence of TTS voice significantly increases consumers' perceptions of flow (a construct depicting a user's interaction with a computer as playful and exploratory), while 3D avatars enhance consumers' feelings of telepresence (a user's experience of seeming to be present in a remote environment by means of a communication medium). These findings offer practitioners guidelines on how to improve interface designs for real-time human-to-human communications on electronic commerce Web sites.