22,723 research outputs found
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
Improving A*OMP: Theoretical and Empirical Analyses With a Novel Dynamic Cost Model
Best-first search has been recently utilized for compressed sensing (CS) by
the A* orthogonal matching pursuit (A*OMP) algorithm. In this work, we
concentrate on theoretical and empirical analyses of A*OMP. We present a
restricted isometry property (RIP) based general condition for exact recovery
of sparse signals via A*OMP. In addition, we develop online guarantees which
promise improved recovery performance with the residue-based termination
instead of the sparsity-based one. We demonstrate the recovery capabilities of
A*OMP with extensive recovery simulations using the adaptive-multiplicative
(AMul) cost model, which effectively compensates for the path length
differences in the search tree. The presented results, involving phase
transitions for different nonzero element distributions as well as recovery
rates and average error, reveal not only the superior recovery accuracy of
A*OMP, but also the improvements with the residue-based termination and the
AMul cost model. Comparison of the run times indicate the speed up by the AMul
cost model. We also demonstrate a hybrid of OMP and A?OMP to accelerate the
search further. Finally, we run A*OMP on a sparse image to illustrate its
recovery performance for more realistic coefcient distributions
Fourier Magnetic Imaging with Nanoscale Resolution and Compressed Sensing Speed-up using Electronic Spins in Diamond
Optically-detected magnetic resonance using Nitrogen Vacancy (NV) color
centres in diamond is a leading modality for nanoscale magnetic field imaging,
as it provides single electron spin sensitivity, three-dimensional resolution
better than 1 nm, and applicability to a wide range of physical and biological
samples under ambient conditions. To date, however, NV-diamond magnetic imaging
has been performed using real space techniques, which are either limited by
optical diffraction to 250 nm resolution or require slow, point-by-point
scanning for nanoscale resolution, e.g., using an atomic force microscope,
magnetic tip, or super-resolution optical imaging. Here we introduce an
alternative technique of Fourier magnetic imaging using NV-diamond. In analogy
with conventional magnetic resonance imaging (MRI), we employ pulsed magnetic
field gradients to phase-encode spatial information on NV electronic spins in
wavenumber or k-space followed by a fast Fourier transform to yield real-space
images with nanoscale resolution, wide field-of-view (FOV), and compressed
sensing speed-up.Comment: 31 pages, 10 figure
Lossy Compressive Sensing Based on Online Dictionary Learning
In this paper, a lossy compression of hyperspectral images is realized by using a novel online dictionary learning method in which three dimensional datasets can be compressed. This online dictionary learning method and blind compressive sensing (BCS) algorithm are combined in a hybrid lossy compression framework for the first time in the literature. According to the experimental results, BCS algorithm has the best compression performance when the compression bit rate is higher than or equal to 0.5 bps. Apart from observing rate-distortion performance, anomaly detection performance is also tested on the reconstructed images to measure the information preservation performance
Array imaging of localized objects in homogeneous and heterogeneous media
We present a comprehensive study of the resolution and stability properties
of sparse promoting optimization theories applied to narrow band array imaging
of localized scatterers. We consider homogeneous and heterogeneous media, and
multiple and single scattering situations. When the media is homogeneous with
strong multiple scattering between scatterers, we give a non-iterative
formulation to find the locations and reflectivities of the scatterers from a
nonlinear inverse problem in two steps, using either single or multiple
illuminations. We further introduce an approach that uses the top singular
vectors of the response matrix as optimal illuminations, which improves the
robustness of sparse promoting optimization with respect to additive noise.
When multiple scattering is negligible, the optimization problem becomes linear
and can be reduced to a hybrid- method when optimal illuminations are
used. When the media is random, and the interaction with the unknown
inhomogeneities can be primarily modeled by wavefront distortions, we address
the statistical stability of these methods. We analyze the fluctuations of the
images obtained with the hybrid- method, and we show that it is stable
with respect to different realizations of the random medium provided the
imaging array is large enough. We compare the performance of the
hybrid- method in random media to the widely used Kirchhoff migration
and the multiple signal classification methods
Single-shot compressed ultrafast photography: a review
Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields
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