Depth Superresolution using Motion Adaptive Regularization

Spatial resolution of depth sensors is often significantly lower compared to that of conventional optical cameras. Recent work has explored the idea of improving the resolution of depth using higher resolution intensity as a side information. In this paper, we demonstrate that further incorporating temporal information in videos can significantly improve the results. In particular, we propose a novel approach that improves depth resolution, exploiting the space-time redundancy in the depth and intensity using motion-adaptive low-rank regularization. Experiments confirm that the proposed approach substantially improves the quality of the estimated high-resolution depth. Our approach can be a first component in systems using vision techniques that rely on high resolution depth information

Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging

Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for solving computational imaging problems through the integration of physical models and learned models. PnP leverages high-fidelity physical sensor models and powerful machine learning methods for prior modeling of data to provide state-of-the-art reconstruction algorithms. PnP algorithms alternate between minimizing a data-fidelity term to promote data consistency and imposing a learned regularizer in the form of an image denoiser. Recent highly-successful applications of PnP algorithms include bio-microscopy, computerized tomography, magnetic resonance imaging, and joint ptycho-tomography. This article presents a unified and principled review of PnP by tracing its roots, describing its major variations, summarizing main results, and discussing applications in computational imaging. We also point the way towards further developments by discussing recent results on equilibrium equations that formulate the problem associated with PnP algorithms

Effect of structural defects on anomalous ultrasound propagation in solids during second-order phase transitions

The effect of structural defects on the critical ultrasound attenuation and ultrasound velocity dispersion in Ising-like three-dimensional systems is studied. A field-theoretical description of the dynamic effects of acoustic-wave propagation in solids during phase transitions is performed with allowance for both fluctuation and relaxation attenuation mechanisms. The temperature and frequency dependences of the scaling functions of the attenuation coefficient and the ultrasound velocity dispersion are calculated in a two-loop approximation for pure and structurally disordered systems, and their asymptotic behavior in hydrodynamic and critical regions is separated. As compared to a pure system, the presence of structural defects in it is shown to cause a stronger increase in the sound attenuation coefficient and the sound velocity dispersion even in the hydrodynamic region as the critical temperature is reached. As compared to pure analogs, structurally disordered systems should exhibit stronger temperature and frequency dependences of the acoustic characteristics in the critical region.Comment: 7 RevTeX pages, 4 figure

Quantization and Compressive Sensing

Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This book chapter explores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, we consider several forms of scalar quantizers, namely uniform, non-uniform, and 1-bit. We provide performance bounds and fundamental analysis, as well as practical quantizer designs and reconstruction algorithms that account for quantization. Furthermore, we provide an overview of Sigma-Delta ($\Sigma\Delta$) quantization in the compressed sensing context, and also discuss implementation issues, recovery algorithms and performance bounds. As we demonstrate, proper accounting for quantization and careful quantizer design has significant impact in the performance of a compressive acquisition system.Comment: 35 pages, 20 figures, to appear in Springer book "Compressed Sensing and Its Applications", 201

The influence of the mechanically activated amorphous form of calcium gluconate on the metabolism and mineral density of bone tissue in dental implantation in patients with chronic generalized periodontitis

The influence of mechanoactivated (nanodispersed) form of calcium gluconate (inside and locally) on the bone mineral density (BMD) and osteointegration processes in 89 patients aged 35-44 with chronic generalized periodontitis with reduced BMD within T-score from -1.1 to -2.5 SD was evaluated. The clinical condition, indicators of mineral metabolism (Ca, Mg, P) in blood plasma, markers of bone remodeling were studied. The inclusion of traditional training and accepted Protocol of dental implant receiving mechanically activated (nanosized) amorphous form of calcium gluconate in patients with chronic generalized periodontitis with reduced mineral density of bone tissue contributes to the correction of calcium-phosphorus metabolism and bone metabolism with the improvement of osseointegration of dental implants.Проведена оценка влияния механоактивированной (нанодисперсной) формы кальция глюконата (внутрь и местно) на минеральную плотность костной ткани (МПкт) и процессы остеоинтеграции у 89 пациентов в возрасте 35-44 года с хроническим генерализованным пародонтитом со сниженной МПкт в пределах т-score от -1,1 до -2,5 SD. Исследовано клиническое состояние, показатели минерального обмена (Ca, Mg, P) в плазме крови, маркёры костного ремоделирования. Включение в традиционную подготовку и принятый протокол дентальной имплантации приёма механоактивированной (нанодисперсной) аморфной формы кальция глюконата у пациентов с хроническим генерализованным пародонтитом со сниженной минеральной плотностью костной ткани способствует коррекции фосфорно-кальциевого обмена и метаболизма костной ткани с улучшением остеоинтеграции и имплантации зубов

Solving Phase Retrieval with a Learned Reference

Fourier phase retrieval is a classical problem that deals with the recovery of an image from the amplitude measurements of its Fourier coefficients. Conventional methods solve this problem via iterative (alternating) minimization by leveraging some prior knowledge about the structure of the unknown image. The inherent ambiguities about shift and flip in the Fourier measurements make this problem especially difficult; and most of the existing methods use several random restarts with different permutations. In this paper, we assume that a known (learned) reference is added to the signal before capturing the Fourier amplitude measurements. Our method is inspired by the principle of adding a reference signal in holography. To recover the signal, we implement an iterative phase retrieval method as an unrolled network. Then we use back propagation to learn the reference that provides us the best reconstruction for a fixed number of phase retrieval iterations. We performed a number of simulations on a variety of datasets under different conditions and found that our proposed method for phase retrieval via unrolled network and learned reference provides near-perfect recovery at fixed (small) computational cost. We compared our method with standard Fourier phase retrieval methods and observed significant performance enhancement using the learned reference.Comment: Accepted to ECCV 2020. Code is available at https://github.com/CSIPlab/learnPR_referenc