SRA: Fast Removal of General Multipath for ToF Sensors

A major issue with Time of Flight sensors is the presence of multipath interference. We present Sparse Reflections Analysis (SRA), an algorithm for removing this interference which has two main advantages. First, it allows for very general forms of multipath, including interference with three or more paths, diffuse multipath resulting from Lambertian surfaces, and combinations thereof. SRA removes this general multipath with robust techniques based on $L_1$ optimization. Second, due to a novel dimension reduction, we are able to produce a very fast version of SRA, which is able to run at frame rate. Experimental results on both synthetic data with ground truth, as well as real images of challenging scenes, validate the approach

Deep Burst Denoising

Noise is an inherent issue of low-light image capture, one which is exacerbated on mobile devices due to their narrow apertures and small sensors. One strategy for mitigating noise in a low-light situation is to increase the shutter time of the camera, thus allowing each photosite to integrate more light and decrease noise variance. However, there are two downsides of long exposures: (a) bright regions can exceed the sensor range, and (b) camera and scene motion will result in blurred images. Another way of gathering more light is to capture multiple short (thus noisy) frames in a "burst" and intelligently integrate the content, thus avoiding the above downsides. In this paper, we use the burst-capture strategy and implement the intelligent integration via a recurrent fully convolutional deep neural net (CNN). We build our novel, multiframe architecture to be a simple addition to any single frame denoising model, and design to handle an arbitrary number of noisy input frames. We show that it achieves state of the art denoising results on our burst dataset, improving on the best published multi-frame techniques, such as VBM4D and FlexISP. Finally, we explore other applications of image enhancement by integrating content from multiple frames and demonstrate that our DNN architecture generalizes well to image super-resolution

LICOR-Liquid Columns' Resonances

The aim of the experiment LICOR was the investigation of the axial resonances oi cylindrical liquid columns supported by equal circular coaxiaJ disks. In preparation ot the D-2 experiment a •heoreiical model has been developed, which exactly describes the small amplitude oscillations of finite cylindrical columns between coaxial circular disks. In addition, in terrestrial experiments the resonance frequencies of small liquid columns with up to 5 mm in diameter have been determined and investigations with density-matched liquids (silicon oil in a waierlmethanol mixture) have been performed. For the D-2 experiment LICOR the front disk and the rear disk lor use in the AFPM have been constructed and equipped with pressure sensors and the necessary electronics. The pressure exerted by the oscillating liquid column on trie supporting disks vsas as low as 10 Pa. Since the data downlink of the Materials Research Laboratory was just one signal oer second and channel, it was necessary to determine amplitude and phase of the pressure already in the LICOR disks. The D-2 experiment has been successfully performed. It has fully confirmed the theoretical models and remarkably supplements the experiments on small liquid columns and on density-matched columns

Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset

Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by time-of-flight cameras. We propose a two-stage, deep-learning approach to address all of these sources of artifacts simultaneously. We also introduce FLAT, a synthetic dataset of 2000 ToF measurements that capture all of these nonidealities, and allows to simulate different camera hardware. Using the Kinect 2 camera as a baseline, we show improved reconstruction errors over state-of-the-art methods, on both simulated and real data.Comment: ECCV 201

On the mixing property for a class of states of relativistic quantum fields

Let $\omega$ be a factor state on the quasi-local algebra $\cal{A}$ of observables generated by a relativistic quantum field, which in addition satisfies certain regularity conditions (satisfied by ground states and the recently constructed thermal states of the $P(\phi)_2$ theory). We prove that there exist space and time translation invariant states, some of which are arbitrarily close to $\omega$ in the weak* topology, for which the time evolution is weakly asymptotically abelian

Convolutional sparse coding for high dynamic range imaging

Current HDR acquisition techniques are based on either (i) fusing multibracketed, low dynamic range (LDR) images, (ii) modifying existing hardware and capturing different exposures simultaneously with multiple sensors, or (iii) reconstructing a single image with spatially-varying pixel exposures. In this paper, we propose a novel algorithm to recover high-quality HDRI images from a single, coded exposure. The proposed reconstruction method builds on recently-introduced ideas of convolutional sparse coding (CSC); this paper demonstrates how to make CSC practical for HDR imaging. We demonstrate that the proposed algorithm achieves higher-quality reconstructions than alternative methods, we evaluate optical coding schemes, analyze algorithmic parameters, and build a prototype coded HDR camera that demonstrates the utility of convolutional sparse HDRI coding with a custom hardware platform

Maximizing nearest neighbour entanglement in finitely correlated qubit--chains

We consider translationally invariant states of an infinite one dimensional chain of qubits or spin-1/2 particles. We maximize the entanglement shared by nearest neighbours via a variational approach based on finitely correlated states. We find an upper bound of nearest neighbour concurrence equal to C=0.434095 which is 0.09% away from the bound C_W=0.434467 obtained by a completely different procedure. The obtained state maximizing nearest neighbour entanglement seems to approximate the maximally entangled mixed states (MEMS). Further we investigate in detail several other properties of the so obtained optimal state.Comment: 12 pages, 4 figures, 2nd version minor change

New order parameters in the Potts model on a Cayley tree

For the $q-$state Potts model new order parameters projecting on a group of spins instead of a single spin are introduced. On a Cayley tree this allows the physical interpretation of the Potts model at noninteger values q of the number of states. The model can be solved recursively. This recursion exhibits chaotic behaviour changing qualitatively at critical values of $q_0$ . Using an additional order parameter belonging to a group of zero extrapolated size the additional ordering is related to a percolation problem. This percolation distinguishes different phases and explains the critical indices of percolation class occuring at the Peierls temperature.Comment: 16 pages TeX, 5 figures PostScrip

Mechanisms of spin-polarized current-driven magnetization switching

The mechanisms of the magnetization switching of magnetic multilayers driven by a current are studied by including exchange interaction between local moments and spin accumulation of conduction electrons. It is found that this exchange interaction leads to two additional terms in the Landau-Lifshitz-Gilbert equation: an effective field and a spin torque. Both terms are proportional to the transverse spin accumulation and have comparable magnitudes

Near-infrared spectroscopy as a diagnostic tool for necrotizing enterocolitis in preterm infants

BACKGROUND: We aimed to investigate whether splanchnic tissue oxygen saturation (rsSO2) measured by near-infrared spectroscopy (NIRS) could contribute to the early diagnosis of necrotizing enterocolitis (NEC). METHODS: We retrospectively included infants with suspected NEC, gestational age <32 weeks and/or birth weight <1200 g in the first 3 weeks after birth. We calculated mean rsSO2, cerebral tissue oxygen saturation (rcSO2), variability of rsSO2 (coefficients of variation [rsCoVAR] = SD/mean), and splanchnic-cerebral oxygenation ratio ([SCOR] = rsSO2/rcSO2) in the period around the abdominal radiograph to confirm or reject NEC. RESULTS: Of the 75 infants, 21 (28%) had NEC (Bell's stage ≥2). Characteristics of infants with and without NEC differed only on mechanical ventilation and nil-per-os status. RsSO2 tended to be higher and rcSO2 lower in infants with NEC. RsCoVAR (median [range]) was lower (0.11 [0.03-0.34]) vs. 0.20 [0.01-0.52], P = 0.002) and SCOR higher (0.64 [0.37-1.36]) vs. 0.47 [0.16-1.09], P = 0.004) in NEC infants. Adjusted for postnatal age, mechanical ventilation, and nil-per-os status, a 0.1 higher rsCoVAR decreased the likelihood of NEC diagnosis with likelihood ratio (LR) 0.38 (95% CI 0.18-0.78) and a 0.1 higher SCOR increased it with LR 1.28 (1.02-1.61). CONCLUSIONS: Using NIRS, high SCOR may confirm NEC and high variability of rsSO2 may rule out NEC, when suspicion arises. IMPACT: Near-infrared spectroscopy may contribute to the diagnosis of necrotizing enterocolitis.When clinical signs are present a high splanchnic-cerebral oxygenation may indicate necrotizing enterocolitis.A low splanchnic-cerebral oxygenation ratio and high variability of splanchnic tissue oxygen saturation may rule out necrotizing enterocolitis.Whether a bedside real-time availability of the splanchnic-cerebral oxygenation ratio and variability of splanchnic tissue oxygen saturation improves NEC diagnosis needs to be further investigated