3,167 research outputs found
Cavlectometry: Towards Holistic Reconstruction of Large Mirror Objects
We introduce a method based on the deflectometry principle for the
reconstruction of specular objects exhibiting significant size and geometric
complexity. A key feature of our approach is the deployment of an Automatic
Virtual Environment (CAVE) as pattern generator. To unfold the full power of
this extraordinary experimental setup, an optical encoding scheme is developed
which accounts for the distinctive topology of the CAVE. Furthermore, we devise
an algorithm for detecting the object of interest in raw deflectometric images.
The segmented foreground is used for single-view reconstruction, the background
for estimation of the camera pose, necessary for calibrating the sensor system.
Experiments suggest a significant gain of coverage in single measurements
compared to previous methods. To facilitate research on specular surface
reconstruction, we will make our data set publicly available
Photon-Efficient Computational 3D and Reflectivity Imaging with Single-Photon Detectors
Capturing depth and reflectivity images at low light levels from active
illumination of a scene has wide-ranging applications. Conventionally, even
with single-photon detectors, hundreds of photon detections are needed at each
pixel to mitigate Poisson noise. We develop a robust method for estimating
depth and reflectivity using on the order of 1 detected photon per pixel
averaged over the scene. Our computational imager combines physically accurate
single-photon counting statistics with exploitation of the spatial correlations
present in real-world reflectivity and 3D structure. Experiments conducted in
the presence of strong background light demonstrate that our computational
imager is able to accurately recover scene depth and reflectivity, while
traditional maximum-likelihood based imaging methods lead to estimates that are
highly noisy. Our framework increases photon efficiency 100-fold over
traditional processing and also improves, somewhat, upon first-photon imaging
under a total acquisition time constraint in raster-scanned operation. Thus our
new imager will be useful for rapid, low-power, and noise-tolerant active
optical imaging, and its fixed dwell time will facilitate parallelization
through use of a detector array.Comment: 11 pages, 8 figure
Locating and quantifying gas emission sources using remotely obtained concentration data
We describe a method for detecting, locating and quantifying sources of gas
emissions to the atmosphere using remotely obtained gas concentration data; the
method is applicable to gases of environmental concern. We demonstrate its
performance using methane data collected from aircraft. Atmospheric point
concentration measurements are modelled as the sum of a spatially and
temporally smooth atmospheric background concentration, augmented by
concentrations due to local sources. We model source emission rates with a
Gaussian mixture model and use a Markov random field to represent the
atmospheric background concentration component of the measurements. A Gaussian
plume atmospheric eddy dispersion model represents gas dispersion between
sources and measurement locations. Initial point estimates of background
concentrations and source emission rates are obtained using mixed L2-L1
optimisation over a discretised grid of potential source locations. Subsequent
reversible jump Markov chain Monte Carlo inference provides estimated values
and uncertainties for the number, emission rates and locations of sources
unconstrained by a grid. Source area, atmospheric background concentrations and
other model parameters are also estimated. We investigate the performance of
the approach first using a synthetic problem, then apply the method to real
data collected from an aircraft flying over: a 1600 km^2 area containing two
landfills, then a 225 km^2 area containing a gas flare stack
Superresolution without Separation
This paper provides a theoretical analysis of diffraction-limited
superresolution, demonstrating that arbitrarily close point sources can be
resolved in ideal situations. Precisely, we assume that the incoming signal is
a linear combination of M shifted copies of a known waveform with unknown
shifts and amplitudes, and one only observes a finite collection of evaluations
of this signal. We characterize properties of the base waveform such that the
exact translations and amplitudes can be recovered from 2M + 1 observations.
This recovery is achieved by solving a a weighted version of basis pursuit over
a continuous dictionary. Our methods combine classical polynomial interpolation
techniques with contemporary tools from compressed sensing.Comment: 23 pages, 8 figure
Underwater image restoration: super-resolution and deblurring via sparse representation and denoising by means of marine snow removal
Underwater imaging has been widely used as a tool in many fields, however, a major issue is the quality of the resulting images/videos. Due to the light's interaction with water and its constituents, the acquired underwater images/videos often suffer from a significant amount of scatter (blur, haze) and noise. In the light of these issues, this thesis considers problems of low-resolution, blurred and noisy underwater images and proposes several approaches to improve the quality of such images/video frames.
Quantitative and qualitative experiments validate the success of proposed algorithms
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