3,449 research outputs found
Simultaneous use of Individual and Joint Regularization Terms in Compressive Sensing: Joint Reconstruction of Multi-Channel Multi-Contrast MRI Acquisitions
Purpose: A time-efficient strategy to acquire high-quality multi-contrast
images is to reconstruct undersampled data with joint regularization terms that
leverage common information across contrasts. However, these terms can cause
leakage of uncommon features among contrasts, compromising diagnostic utility.
The goal of this study is to develop a compressive sensing method for
multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally
utilizes shared information while preventing feature leakage.
Theory: Joint regularization terms group sparsity and colour total variation
are used to exploit common features across images while individual sparsity and
total variation are also used to prevent leakage of distinct features across
contrasts. The multi-channel multi-contrast reconstruction problem is solved
via a fast algorithm based on Alternating Direction Method of Multipliers.
Methods: The proposed method is compared against using only individual and
only joint regularization terms in reconstruction. Comparisons were performed
on single-channel simulated and multi-channel in-vivo datasets in terms of
reconstruction quality and neuroradiologist reader scores.
Results: The proposed method demonstrates rapid convergence and improved
image quality for both simulated and in-vivo datasets. Furthermore, while
reconstructions that solely use joint regularization terms are prone to
leakage-of-features, the proposed method reliably avoids leakage via
simultaneous use of joint and individual terms.
Conclusion: The proposed compressive sensing method performs fast
reconstruction of multi-channel multi-contrast MRI data with improved image
quality. It offers reliability against feature leakage in joint
reconstructions, thereby holding great promise for clinical use.Comment: 13 pages, 13 figures. Submitted for possible publicatio
Robust Deep Compressive Sensing with Recurrent-Residual Structural Constraints
Existing deep compressive sensing (CS) methods either ignore adaptive online
optimization or depend on costly iterative optimizer during reconstruction.
This work explores a novel image CS framework with recurrent-residual
structural constraint, termed as RCS-NET. The RCS-NET first
progressively optimizes the acquired samplings through a novel recurrent neural
network. The cascaded residual convolutional network then fully reconstructs
the image from optimized latent representation. As the first deep CS framework
efficiently bridging adaptive online optimization, the RCS-NET integrates
the robustness of online optimization with the efficiency and nonlinear
capacity of deep learning methods. Signal correlation has been addressed
through the network architecture. The adaptive sensing nature further makes it
an ideal candidate for color image CS via leveraging channel correlation.
Numerical experiments verify the proposed recurrent latent optimization design
not only fulfills the adaptation motivation, but also outperforms classic long
short-term memory (LSTM) architecture in the same scenario. The overall
framework demonstrates hardware implementation feasibility, with leading
robustness and generalization capability among existing deep CS benchmarks
Plug-and-Play Algorithms for Video Snapshot Compressive Imaging
We consider the reconstruction problem of video snapshot compressive imaging
(SCI), which captures high-speed videos using a low-speed 2D sensor (detector).
The underlying principle of SCI is to modulate sequential high-speed frames
with different masks and then these encoded frames are integrated into a
snapshot on the sensor and thus the sensor can be of low-speed. On one hand,
video SCI enjoys the advantages of low-bandwidth, low-power and low-cost. On
the other hand, applying SCI to large-scale problems (HD or UHD videos) in our
daily life is still challenging and one of the bottlenecks lies in the
reconstruction algorithm. Exiting algorithms are either too slow (iterative
optimization algorithms) or not flexible to the encoding process (deep learning
based end-to-end networks). In this paper, we develop fast and flexible
algorithms for SCI based on the plug-and-play (PnP) framework. In addition to
the PnP-ADMM method, we further propose the PnP-GAP (generalized alternating
projection) algorithm with a lower computational workload. We first employ the
image deep denoising priors to show that PnP can recover a UHD color video with
30 frames from a snapshot measurement. Since videos have strong temporal
correlation, by employing the video deep denoising priors, we achieve a
significant improvement in the results. Furthermore, we extend the proposed PnP
algorithms to the color SCI system using mosaic sensors, where each pixel only
captures the red, green or blue channels. A joint reconstruction and
demosaicing paradigm is developed for flexible and high quality reconstruction
of color video SCI systems. Extensive results on both simulation and real
datasets verify the superiority of our proposed algorithm.Comment: 18 pages, 12 figures and 4 tables. Journal extension of
arXiv:2003.13654. Code available at
https://github.com/liuyang12/PnP-SCI_pytho
Compressive sensing based Bayesian sparse channel estimation for OFDM communication systems: high performance and low complexity
In orthogonal frequency division modulation (OFDM) communication systems,
channel state information (CSI) is required at receiver due to the fact that
frequency-selective fading channel leads to disgusting inter-symbol
interference (ISI) over data transmission. Broadband channel model is often
described by very few dominant channel taps and they can be probed by
compressive sensing based sparse channel estimation (SCE) methods, e.g.,
orthogonal matching pursuit algorithm, which can take the advantage of sparse
structure effectively in the channel as for prior information. However, these
developed methods are vulnerable to both noise interference and column
coherence of training signal matrix. In other words, the primary objective of
these conventional methods is to catch the dominant channel taps without a
report of posterior channel uncertainty. To improve the estimation performance,
we proposed a compressive sensing based Bayesian sparse channel estimation
(BSCE) method which can not only exploit the channel sparsity but also mitigate
the unexpected channel uncertainty without scarifying any computational
complexity. The propose method can reveal potential ambiguity among multiple
channel estimators that are ambiguous due to observation noise or correlation
interference among columns in the training matrix. Computer simulations show
that propose method can improve the estimation performance when comparing with
conventional SCE methods.Comment: 24 pages,16 figures, submitted for a journa
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