8,640 research outputs found
Compressive Time-of-Flight 3D Imaging Using Block-Structured Sensing Matrices
Spatially and temporally highly resolved depth information enables numerous
applications including human-machine interaction in gaming or safety functions
in the automotive industry. In this paper, we address this issue using
Time-of-flight (ToF) 3D cameras which are compact devices providing highly
resolved depth information. Practical restrictions often require to reduce the
amount of data to be read-out and transmitted. Using standard ToF cameras, this
can only be achieved by lowering the spatial or temporal resolution. To
overcome such a limitation, we propose a compressive ToF camera design using
block-structured sensing matrices that allows to reduce the amount of data
while keeping high spatial and temporal resolution. We propose the use of
efficient reconstruction algorithms based on l^1-minimization and
TV-regularization. The reconstruction methods are applied to data captured by a
real ToF camera system and evaluated in terms of reconstruction quality and
computational effort. For both, l^1-minimization and TV-regularization, we use
a local as well as a global reconstruction strategy. For all considered
instances, global TV-regularization turns out to clearly perform best in terms
of evaluation metrics including the PSNR.Comment: According to a suggestion, we changed the old title "A Framework for
Compressive Time-of-Flight 3D Sensing" to "Compressive Time-of-Flight 3D
Imaging Using Block-Structured Sensing Matrices
Compressive Sensing of Large-Scale Images: An Assumption-Free Approach
Cost-efficient compressive sensing of big media data with fast reconstructed
high-quality results is very challenging. In this paper, we propose a new
large-scale image compressive sensing method, composed of operator-based
strategy in the context of fixed point continuation method and weighted LASSO
with tree structure sparsity pattern. The main characteristic of our method is
free from any assumptions and restrictions. The feasibility of our method is
verified via simulations and comparisons with state-of-the-art algorithms.Comment: 8 pages, 5 figure
Robust Coding of Encrypted Images via Structural Matrix
The robust coding of natural images and the effective compression of
encrypted images have been studied individually in recent years. However,
little work has been done in the robust coding of encrypted images. The
existing results in these two individual research areas cannot be combined
directly for the robust coding of encrypted images. This is because the robust
coding of natural images relies on the elimination of spatial correlations
using sparse transforms such as discrete wavelet transform (DWT), which is
ineffective to encrypted images due to the weak correlation between encrypted
pixels. Moreover, the compression of encrypted images always generates code
streams with different significance. If one or more such streams are lost, the
quality of the reconstructed images may drop substantially or decoding error
may exist, which violates the goal of robust coding of encrypted images. In
this work, we intend to design a robust coder, based on compressive sensing
with structurally random matrix, for encrypted images over packet transmission
networks. The proposed coder can be applied in the scenario that Alice needs a
semi-trusted channel provider Charlie to encode and transmit the encrypted
image to Bob. In particular, Alice first encrypts an image using globally
random permutation and then sends the encrypted image to Charlie who samples
the encrypted image using a structural matrix. Through an imperfect channel
with packet loss, Bob receives the compressive measurements and reconstructs
the original image by joint decryption and decoding. Experimental results show
that the proposed coder can be considered as an efficient multiple description
coder with a number of descriptions against packet loss.Comment: 10 pages, 11 figure
Dictionary and Image Recovery from Incomplete and Random Measurements
This paper tackles algorithmic and theoretical aspects of dictionary learning
from incomplete and random block-wise image measurements and the performance of
the adaptive dictionary for sparse image recovery. This problem is related to
blind compressed sensing in which the sparsifying dictionary or basis is viewed
as an unknown variable and subject to estimation during sparse recovery.
However, unlike existing guarantees for a successful blind compressed sensing,
our results do not rely on additional structural constraints on the learned
dictionary or the measured signal. In particular, we rely on the spatial
diversity of compressive measurements to guarantee that the solution is unique
with a high probability. Moreover, our distinguishing goal is to measure and
reduce the estimation error with respect to the ideal dictionary that is based
on the complete image. Using recent results from random matrix theory, we show
that applying a slightly modified dictionary learning algorithm over
compressive measurements results in accurate estimation of the ideal dictionary
for large-scale images. Empirically, we experiment with both space-invariant
and space-varying sensing matrices and demonstrate the critical role of spatial
diversity in measurements. Simulation results confirm that the presented
algorithm outperforms the typical non-adaptive sparse recovery based on
offline-learned universal dictionaries
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements
The goal of this paper is to present a non-iterative and more importantly an
extremely fast algorithm to reconstruct images from compressively sensed (CS)
random measurements. To this end, we propose a novel convolutional neural
network (CNN) architecture which takes in CS measurements of an image as input
and outputs an intermediate reconstruction. We call this network, ReconNet. The
intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the
final reconstructed image. On a standard dataset of images we show significant
improvements in reconstruction results (both in terms of PSNR and time
complexity) over state-of-the-art iterative CS reconstruction algorithms at
various measurement rates. Further, through qualitative experiments on real
data collected using our block single pixel camera (SPC), we show that our
network is highly robust to sensor noise and can recover visually better
quality images than competitive algorithms at extremely low sensing rates of
0.1 and 0.04. To demonstrate that our algorithm can recover semantically
informative images even at a low measurement rate of 0.01, we present a very
robust proof of concept real-time visual tracking application.Comment: Accepted at IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR), 201
LAMP: A Locally Adapting Matching Pursuit Framework for Group Sparse Signatures in Ultra-Wide Band Radar Imaging
It has been found that radar returns of extended targets are not only sparse
but also exhibit a tendency to cluster into randomly located, variable sized
groups. However, the standard techniques of Compressive Sensing as applied in
radar imaging hardly considers the clustering tendency into account while
reconstructing the image from the compressed measurements. If the group
sparsity is taken into account, it is intuitive that one might obtain better
results both in terms of accuracy and time complexity as compared to the
conventional recovery techniques like Orthogonal Matching Pursuit (OMP). In
order to remedy this, techniques like Block OMP have been used in the existing
literature. An alternate approach is via reconstructing the signal by
transforming into the Hough Transform Domain where they become point-wise
sparse. However, these techniques essentially assume specific size and
structure of the groups and are not always effective if the exact
characteristics of the groups are not known, prior to reconstruction. In this
manuscript, a novel framework that we call locally adapting matching pursuit
(LAMP) have been proposed for efficient reconstruction of group sparse signals
from compressed measurements without assuming any specific size, location, or
structure of the groups. The recovery guarantee of the LAMP and its superiority
compared to the existing algorithms has been established with respect to
accuracy, time complexity and flexibility in group size. LAMP has been
successfully used on a real-world, experimental data set.Comment: 14 pages,22 figures, Draft to be submitted to journa
Fast Greedy Approaches for Compressive Sensing of Large-Scale Signals
Cost-efficient compressive sensing is challenging when facing large-scale
data, {\em i.e.}, data with large sizes. Conventional compressive sensing
methods for large-scale data will suffer from low computational efficiency and
massive memory storage. In this paper, we revisit well-known solvers called
greedy algorithms, including Orthogonal Matching Pursuit (OMP), Subspace
Pursuit (SP), Orthogonal Matching Pursuit with Replacement (OMPR). Generally,
these approaches are conducted by iteratively executing two main steps: 1)
support detection and 2) solving least square problem.
To reduce the cost of Step 1, it is not hard to employ the sensing matrix
that can be implemented by operator-based strategy instead of matrix-based one
and can be speeded by fast Fourier Transform (FFT). Step 2, however, requires
maintaining and calculating a pseudo-inverse of a sub-matrix, which is random
and not structural, and, thus, operator-based matrix does not work. To overcome
this difficulty, instead of solving Step 2 by a closed-form solution, we
propose a fast and cost-effective least square solver, which combines a
Conjugate Gradient (CG) method with our proposed weighted least square problem
to iteratively approximate the ground truth yielded by a greedy algorithm.
Extensive simulations and theoretical analysis validate that the proposed
method is cost-efficient and is readily incorporated with the existing greedy
algorithms to remarkably improve the performance for large-scale problems.Comment: 10 pages, 3 figures, 4 table
Convolutional Neural Networks for Non-iterative Reconstruction of Compressively Sensed Images
Traditional algorithms for compressive sensing recovery are computationally
expensive and are ineffective at low measurement rates. In this work, we
propose a data driven non-iterative algorithm to overcome the shortcomings of
earlier iterative algorithms. Our solution, ReconNet, is a deep neural network,
whose parameters are learned end-to-end to map block-wise compressive
measurements of the scene to the desired image blocks. Reconstruction of an
image becomes a simple forward pass through the network and can be done in
real-time. We show empirically that our algorithm yields reconstructions with
higher PSNRs compared to iterative algorithms at low measurement rates and in
presence of measurement noise. We also propose a variant of ReconNet which uses
adversarial loss in order to further improve reconstruction quality. We discuss
how adding a fully connected layer to the existing ReconNet architecture allows
for jointly learning the measurement matrix and the reconstruction algorithm in
a single network. Experiments on real data obtained from a block compressive
imager show that our networks are robust to unseen sensor noise. Finally,
through an experiment in object tracking, we show that even at very low
measurement rates, reconstructions using our algorithm possess rich semantic
content that can be used for high level inference
Multi-Structural Signal Recovery for Biomedical Compressive Sensing
Compressive sensing has shown significant promise in biomedical fields. It
reconstructs a signal from sub-Nyquist random linear measurements. Classical
methods only exploit the sparsity in one domain. A lot of biomedical signals
have additional structures, such as multi-sparsity in different domains,
piecewise smoothness, low rank, etc. We propose a framework to exploit all the
available structure information. A new convex programming problem is generated
with multiple convex structure-inducing constraints and the linear measurement
fitting constraint. With additional a priori information for solving the
underdetermined system, the signal recovery performance can be improved. In
numerical experiments, we compare the proposed method with classical methods.
Both simulated data and real-life biomedical data are used. Results show that
the newly proposed method achieves better reconstruction accuracy performance
in term of both L1 and L2 errors.Comment: 29 pages, 20 figures, accepted by IEEE Transactions on Biomedical
Engineering. Online first version:
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6519288&tag=
Scan-based Compressed Terahertz Imaging and Real-Time Reconstruction via the Complex-valued Fast Block Sparse Bayesian Learning Algorithm
Compressed Sensing based Terahertz imaging (CS-THz) is a computational
imaging technique. It uses only one THz receiver to accumulate the random
modulated image measurements where the original THz image is reconstruct from
these measurements using compressed sensing solvers. The advantage of the
CS-THz is its reduced acquisition time compared with the raster scan mode.
However, when it applied to large-scale two-dimensional (2D) imaging, the
increased dimension resulted in both high computational complexity and
excessive memory usage. In this paper, we introduced a novel CS-based THz
imaging system that progressively compressed the THz image column by column.
Therefore, the CS-THz system could be simplified with a much smaller sized
modulator and reduced dimension. In order to utilize the block structure and
the correlation of adjacent columns of the THz image, a complex-valued block
sparse Bayesian learning algorithm was proposed. We conducted systematic
evaluation of state-of-the-art CS algorithms under the scan based CS-THz
architecture. The compression ratios and the choices of the sensing matrices
were analyzed in detail using both synthetic and real-life THz images.
Simulation results showed that both the scan based architecture and the
proposed recovery algorithm were superior and efficient for large scale CS-THz
applications
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