264 research outputs found
Cygnus A super-resolved via convex optimisation from VLA data
We leverage the Sparsity Averaging Reweighted Analysis (SARA) approach for
interferometric imaging, that is based on convex optimisation, for the
super-resolution of Cyg A from observations at the frequencies 8.422GHz and
6.678GHz with the Karl G. Jansky Very Large Array (VLA). The associated average
sparsity and positivity priors enable image reconstruction beyond instrumental
resolution. An adaptive Preconditioned Primal-Dual algorithmic structure is
developed for imaging in the presence of unknown noise levels and calibration
errors. We demonstrate the superior performance of the algorithm with respect
to the conventional CLEAN-based methods, reflected in super-resolved images
with high fidelity. The high resolution features of the recovered images are
validated by referring to maps of Cyg A at higher frequencies, more precisely
17.324GHz and 14.252GHz. We also confirm the recent discovery of a radio
transient in Cyg A, revealed in the recovered images of the investigated data
sets. Our matlab code is available online on GitHub.Comment: 14 pages, 7 figures (3/7 animated figures), accepted for publication
in MNRA
Unsupervised Superpixel Generation using Edge-Sparse Embedding
Partitioning an image into superpixels based on the similarity of pixels with
respect to features such as colour or spatial location can significantly reduce
data complexity and improve subsequent image processing tasks. Initial
algorithms for unsupervised superpixel generation solely relied on local cues
without prioritizing significant edges over arbitrary ones. On the other hand,
more recent methods based on unsupervised deep learning either fail to properly
address the trade-off between superpixel edge adherence and compactness or lack
control over the generated number of superpixels. By using random images with
strong spatial correlation as input, \ie, blurred noise images, in a
non-convolutional image decoder we can reduce the expected number of contrasts
and enforce smooth, connected edges in the reconstructed image. We generate
edge-sparse pixel embeddings by encoding additional spatial information into
the piece-wise smooth activation maps from the decoder's last hidden layer and
use a standard clustering algorithm to extract high quality superpixels. Our
proposed method reaches state-of-the-art performance on the BSDS500,
PASCAL-Context and a microscopy dataset
High-Order Sparsity Exploiting Methods with Applications in Imaging and PDEs
abstract: High-order methods are known for their accuracy and computational performance when applied to solving partial differential equations and have widespread use
in representing images compactly. Nonetheless, high-order methods have difficulty representing functions containing discontinuities or functions having slow spectral decay in the chosen basis. Certain sensing techniques such as MRI and SAR provide data in terms of Fourier coefficients, and thus prescribe a natural high-order basis. The field of compressed sensing has introduced a set of techniques based on regularization that promote sparsity and facilitate working with functions having discontinuities. In this dissertation, high-order methods and regularization are used to address three problems: reconstructing piecewise smooth functions from sparse and and noisy Fourier data, recovering edge locations in piecewise smooth functions from sparse and noisy Fourier data, and reducing time-stepping constraints when numerically solving certain time-dependent hyperbolic partial differential equations.Dissertation/ThesisDoctoral Dissertation Applied Mathematics 201
Data-Driven Image Restoration
Every day many images are taken by digital cameras, and people
are demanding visually accurate and pleasing result. Noise and
blur degrade images captured by modern cameras, and high-level
vision tasks (such as segmentation, recognition, and tracking)
require high-quality images. Therefore, image restoration
specifically, image
deblurring and image denoising is a critical preprocessing step.
A fundamental problem in image deblurring is to recover reliably
distinct spatial frequencies that have been suppressed by the
blur kernel. Existing image deblurring techniques often rely on
generic image priors that only help recover part of the frequency
spectrum, such as the frequencies near the high-end. To this end,
we pose the following specific questions: (i) Does class-specific
information offer an advantage over existing generic priors for
image quality restoration? (ii) If a class-specific prior exists,
how should it be encoded into a deblurring framework to recover
attenuated image frequencies? Throughout this work, we devise a
class-specific prior based on the band-pass filter responses and
incorporate it into a deblurring strategy. Specifically, we show
that the subspace of band-pass filtered images and their
intensity distributions serve as useful priors for recovering
image frequencies.
Next, we present a novel image denoising algorithm that uses
external, category specific image database. In contrast to
existing noisy image restoration algorithms, our method selects
clean image “support patches” similar to the noisy patch from
an external database. We employ a content adaptive distribution
model for each patch where we derive the parameters of the
distribution from the support patches. Our objective function
composed of a Gaussian fidelity term that imposes category
specific information, and a low-rank term that encourages the
similarity between the noisy and the support patches in a robust
manner.
Finally, we propose to learn a fully-convolutional network model
that consists of a Chain of Identity Mapping Modules (CIMM) for
image denoising. The CIMM structure possesses two distinctive
features that are important for the noise removal task. Firstly,
each residual unit employs identity mappings as the skip
connections and receives pre-activated input to preserve the
gradient magnitude propagated in both the forward and backward
directions. Secondly, by utilizing dilated kernels for the
convolution layers in the residual branch, each neuron in the
last convolution layer of each module can observe the full
receptive field of the first layer
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Sub-Nanosecond Time of Flight on Commercial Wi-Fi Cards
Time-of-flight, i.e., the time incurred by a signal to travel from
transmitter to receiver, is perhaps the most intuitive way to measure distances
using wireless signals. It is used in major positioning systems such as GPS,
RADAR, and SONAR. However, attempts at using time-of-flight for indoor
localization have failed to deliver acceptable accuracy due to fundamental
limitations in measuring time on Wi-Fi and other RF consumer technologies.
While the research community has developed alternatives for RF-based indoor
localization that do not require time-of-flight, those approaches have their
own limitations that hamper their use in practice. In particular, many existing
approaches need receivers with large antenna arrays while commercial Wi-Fi
nodes have two or three antennas. Other systems require fingerprinting the
environment to create signal maps. More fundamentally, none of these methods
support indoor positioning between a pair of Wi-Fi devices
without~third~party~support.
In this paper, we present a set of algorithms that measure the time-of-flight
to sub-nanosecond accuracy on commercial Wi-Fi cards. We implement these
algorithms and demonstrate a system that achieves accurate device-to-device
localization, i.e. enables a pair of Wi-Fi devices to locate each other without
any support from the infrastructure, not even the location of the access
points.Comment: 14 page
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