Time-of-flight (ToF) cameras calculate depth maps by reconstructing phase shifts of amplitudemodulated signals. For broad illumination or transparent objects, reflections from multiple scene points can illuminate a given pixel, giving rise to an erroneous depth map. We report here a sparsity regularized solution that separates K interfering components using multiple modulation frequency measurements. The method maps ToF imaging to the general framework of spectral estimation theory and has applications in improving depth profiles and exploiting multiple scattering.
Multipath interference (MPI) is one of the major sources of both depth and amplitude measurement errors in Time-of-Flight (ToF) cameras. This problem has seen a lot of attention recently. In this work, we discuss the MPI problem within the framework spectral estimation theory and multifrequency measurements. As compared to previous approaches that consider up to two interfering paths, our model considers the general case of K-interfering paths. In the theoretical setting, we show that for the case of K-interfering paths of light, 2K + 1 frequency measurements suffice to recover the depth and amplitude values corresponding to each of the K optical paths. What singles out our method is the that our algorithm is non-iterative in implementation. This leads to a closed-form solution which is computationally attractive. Also, for the first time, we demonstrate the effectiveness of our model on an offthe-shelf Microsoft Kinect for the X-Box one.
Objective: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for such things as cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state of the art shear wave imaging techniques, essential to promote this goal, are limited to highend ultrasound hardware due to high power requirements; are extremely sensitive to patient and sonographer motion, and generally, suffer from low frame rates.Motivated by research and theory showing that longitudinal wave sound speed carries similar diagnostic abilities to shear wave imaging, we present an alternative approach using single sided pressure-wave sound speed measurements from channel data.Methods: In this paper, we present a single-sided sound speed inversion solution using a fully convolutional deep neural network. We use simulations for training, allowing the generation of limitless ground truth data.Results: We show that it is possible to invert for longitudinal sound speed in soft tissue at high frame rates. We validate the method on simulated data. We present highly encouraging results on limited real data.Conclusion: Sound speed inversion on channel data has significant potential, made possible in real time with deep learning technologies.Significance: Specialized shear wave ultrasound systems remain inaccessible in many locations. longitudinal sound speed and deep learning technologies enable an alternative approach to diagnosis based on tissue elasticity. High frame rates are possible.Index Terms-deep learning, inverse problems, ultrasound, sound speed inversion
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