33,334 research outputs found
Deep Learning for Single Image Super-Resolution: A Brief Review
Single image super-resolution (SISR) is a notoriously challenging ill-posed
problem, which aims to obtain a high-resolution (HR) output from one of its
low-resolution (LR) versions. To solve the SISR problem, recently powerful deep
learning algorithms have been employed and achieved the state-of-the-art
performance. In this survey, we review representative deep learning-based SISR
methods, and group them into two categories according to their major
contributions to two essential aspects of SISR: the exploration of efficient
neural network architectures for SISR, and the development of effective
optimization objectives for deep SISR learning. For each category, a baseline
is firstly established and several critical limitations of the baseline are
summarized. Then representative works on overcoming these limitations are
presented based on their original contents as well as our critical
understandings and analyses, and relevant comparisons are conducted from a
variety of perspectives. Finally we conclude this review with some vital
current challenges and future trends in SISR leveraging deep learning
algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM
Power spectrum multipoles on the curved sky: an application to the 6-degree Field Galaxy Survey
The peculiar velocities of galaxies cause their redshift-space clustering to
depend on the angle to the line-of-sight, providing a key test of gravitational
physics on cosmological scales. These effects may be described using a
multipole expansion of the clustering measurements. Focussing on Fourier-space
statistics, we present a new analysis of the effect of the survey window
function, and the variation of the line-of-sight across a survey, on the
modelling of power spectrum multipoles. We determine the joint covariance of
the Fourier-space multipoles in a Gaussian approximation, and indicate how
these techniques may be extended to studies of overlapping galaxy populations
via multipole cross-power spectra. We apply our methodology to one of the
widest-area galaxy redshift surveys currently available, the 6-degree Field
Galaxy Survey, deducing a normalized growth rate f*sigma_8(z=0.06) = 0.38 +/-
0.12 in the low-redshift Universe, in agreement with previous analyses of this
dataset using different techniques. Our framework should be useful for
processing future wide-angle galaxy redshift surveys.Comment: 17 pages, 7 figures, version accepted by MNRA
Galaxy shape measurement with convolutional neural networks
We present our results from training and evaluating a convolutional neural
network (CNN) to predict galaxy shapes from wide-field survey images of the
first data release of the Dark Energy Survey (DES DR1). We use conventional
shape measurements as ground truth from an overlapping, deeper survey with less
sky coverage, the Canada-France Hawaii Telescope Lensing Survey (CFHTLenS). We
demonstrate that CNN predictions from single band DES images reproduce the
results of CFHTLenS at bright magnitudes and show higher correlation with
CFHTLenS at fainter magnitudes than maximum likelihood model fitting estimates
in the DES Y1 im3shape catalogue. Prediction of shape parameters with a CNN is
also extremely fast, it takes only 0.2 milliseconds per galaxy, improving more
than 4 orders of magnitudes over forward model fitting. The CNN can also
accurately predict shapes when using multiple images of the same galaxy, even
in different color bands, with no additional computational overhead. The CNN is
again more precise for faint objects, and the advantage of the CNN is more
pronounced for blue galaxies than red ones when compared to the DES Y1
metacalibration catalogue, which fits a single Gaussian profile using riz band
images. We demonstrate that CNN shape predictions within the metacalibration
self-calibrating framework yield shear estimates with negligible multiplicative
bias, , and no significant PSF leakage. Our proposed setup is
applicable to current and next generation weak lensing surveys where higher
quality ground truth shapes can be measured in dedicated deep fields
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