49 research outputs found
Lensless Imaging by Compressive Sensing
In this paper, we propose a lensless compressive imaging architecture. The
architecture consists of two components, an aperture assembly and a sensor. No
lens is used. The aperture assembly consists of a two dimensional array of
aperture elements. The transmittance of each aperture element is independently
controllable. The sensor is a single detection element. A compressive sensing
matrix is implemented by adjusting the transmittance of the individual aperture
elements according to the values of the sensing matrix. The proposed
architecture is simple and reliable because no lens is used. The architecture
can be used for capturing images of visible and other spectra such as infrared,
or millimeter waves, in surveillance applications for detecting anomalies or
extracting features such as speed of moving objects. Multiple sensors may be
used with a single aperture assembly to capture multi-view images
simultaneously. A prototype was built by using a LCD panel and a photoelectric
sensor for capturing images of visible spectrum.Comment: Accepted ICIP 2013. 5 Pages, 7 Figures. arXiv admin note: substantial
text overlap with arXiv:1302.178
Channel-Optimized Vector Quantizer Design for Compressed Sensing Measurements
We consider vector-quantized (VQ) transmission of compressed sensing (CS)
measurements over noisy channels. Adopting mean-square error (MSE) criterion to
measure the distortion between a sparse vector and its reconstruction, we
derive channel-optimized quantization principles for encoding CS measurement
vector and reconstructing sparse source vector. The resulting necessary optimal
conditions are used to develop an algorithm for training channel-optimized
vector quantization (COVQ) of CS measurements by taking the end-to-end
distortion measure into account.Comment: Published in ICASSP 201
Optimal Quantization for Compressive Sensing under Message Passing Reconstruction
We consider the optimal quantization of compressive sensing measurements
following the work on generalization of relaxed belief propagation (BP) for
arbitrary measurement channels. Relaxed BP is an iterative reconstruction
scheme inspired by message passing algorithms on bipartite graphs. Its
asymptotic error performance can be accurately predicted and tracked through
the state evolution formalism. We utilize these results to design mean-square
optimal scalar quantizers for relaxed BP signal reconstruction and empirically
demonstrate the superior error performance of the resulting quantizers.Comment: 5 pages, 3 figures, submitted to IEEE International Symposium on
Information Theory (ISIT) 2011; minor corrections in v
Analysis-by-Synthesis-based Quantization of Compressed Sensing Measurements
We consider a resource-constrained scenario where a compressed sensing- (CS)
based sensor has a low number of measurements which are quantized at a low rate
followed by transmission or storage. Applying this scenario, we develop a new
quantizer design which aims to attain a high-quality reconstruction performance
of a sparse source signal based on analysis-by-synthesis framework. Through
simulations, we compare the performance of the proposed quantization algorithm
vis-a-vis existing quantization methods.Comment: 5 pages, Published in ICASSP 201
Distributed Quantization for Compressed Sensing
We study distributed coding of compressed sensing (CS) measurements using
vector quantizer (VQ). We develop a distributed framework for realizing
optimized quantizer that enables encoding CS measurements of correlated sparse
sources followed by joint decoding at a fusion center. The optimality of VQ
encoder-decoder pairs is addressed by minimizing the sum of mean-square errors
between the sparse sources and their reconstruction vectors at the fusion
center. We derive a lower-bound on the end-to-end performance of the studied
distributed system, and propose a practical encoder-decoder design through an
iterative algorithm.Comment: 5 Pages, Accepted for presentation in ICASSP 201
Random Access in C-RAN for User Activity Detection with Limited-Capacity Fronthaul
Cloud-Radio Access Network (C-RAN) is characterized by a hierarchical
structure in which the baseband processing functionalities of remote radio
heads (RRHs) are implemented by means of cloud computing at a Central Unit
(CU). A key limitation of C-RANs is given by the capacity constraints of the
fronthaul links connecting RRHs to the CU. In this letter, the impact of this
architectural constraint is investigated for the fundamental functions of
random access and active User Equipment (UE) identification in the presence of
a potentially massive number of UEs. In particular, the standard C-RAN approach
based on quantize-and-forward and centralized detection is compared to a scheme
based on an alternative CU-RRH functional split that enables local detection.
Both techniques leverage Bayesian sparse detection. Numerical results
illustrate the relative merits of the two schemes as a function of the system
parameters.Comment: 6 pages, 3 figures, under revision in IEEE Signal Processing Letter
Reconstruction Improvements on Compressed Sensing
This paper presents the design of Improvements on Reconstruction of Compressive Sensed images. The proposed techniques will improve the reconstruction time consumption. Those improvements use techniques including matrix simplification, multi-thread and GPU computations. Implementing those improvements achieve gains on time consumption, compared to the baseline. This paper also presents a novel scheme of buffering steamed image (video) to achieve optimum performance
Optimizing quantization for Lasso recovery
This letter is focused on quantized Compressed Sensing, assuming that Lasso is used for signal estimation. Leveraging recent work, we provide a framework to optimize the quantization function and show that the recovered signal converges to the actual signal at a quadratic rate as a function of the quantization level. We show that when the number of observations is high, this method of quantization gives a significantly better recovery rate than standard Lloyd-Max quantization. We support our theoretical analysis with numerical simulations