33,544 research outputs found
Video Compressive Sensing for Dynamic MRI
We present a video compressive sensing framework, termed kt-CSLDS, to
accelerate the image acquisition process of dynamic magnetic resonance imaging
(MRI). We are inspired by a state-of-the-art model for video compressive
sensing that utilizes a linear dynamical system (LDS) to model the motion
manifold. Given compressive measurements, the state sequence of an LDS can be
first estimated using system identification techniques. We then reconstruct the
observation matrix using a joint structured sparsity assumption. In particular,
we minimize an objective function with a mixture of wavelet sparsity and joint
sparsity within the observation matrix. We derive an efficient convex
optimization algorithm through alternating direction method of multipliers
(ADMM), and provide a theoretical guarantee for global convergence. We
demonstrate the performance of our approach for video compressive sensing, in
terms of reconstruction accuracy. We also investigate the impact of various
sampling strategies. We apply this framework to accelerate the acquisition
process of dynamic MRI and show it achieves the best reconstruction accuracy
with the least computational time compared with existing algorithms in the
literature.Comment: 30 pages, 9 figure
Generation and Analysis of Constrained Random Sampling Patterns
Random sampling is a technique for signal acquisition which is gaining
popularity in practical signal processing systems. Nowadays, event-driven
analog-to-digital converters make random sampling feasible in practical
applications. A process of random sampling is defined by a sampling pattern,
which indicates signal sampling points in time. Practical random sampling
patterns are constrained by ADC characteristics and application requirements.
In this paper authors introduce statistical methods which evaluate random
sampling pattern generators with emphasis on practical applications.
Furthermore, the authors propose a new random pattern generator which copes
with strict practical limitations imposed on patterns, with possibly minimal
loss in randomness of sampling. The proposed generator is compared with
existing sampling pattern generators using the introduced statistical methods.
It is shown that the proposed algorithm generates random sampling patterns
dedicated for event-driven-ADCs better than existed sampling pattern
generators. Finally, implementation issues of random sampling patterns are
discussed.Comment: 29 pages, 12 figures, submitted to Circuits, Systems and Signal
Processing journa
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