2,989 research outputs found
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
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 be a factor state on the quasi-local algebra 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 theory). We prove that
there exist space and time translation invariant states, some of which are
arbitrarily close to 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 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 . 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
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