1,938 research outputs found
First CT-MRI Scanner for Multi-dimensional Synchrony and Multi-physical Coupling
We propose to prototype the first CT-MRI scanner for radiation therapy and
basic research, demonstrate its transformative biomedical potential, and
initiate a paradigm shift in multimodality imaging. Our design consists of a
double donut-shaped pair of permanent magnets to form a regionally uniform
~0.5T magnetic field and leave room for a stationary 9-source interior CT
gantry at 3 tube voltages (triple-energy CT). Image reconstruction will be in a
compressive sensing framework. Please discuss with Dr. Ge Wang
([email protected]) if you are interested in collaborative opportunities.Comment: 2 pages, 5 reference
Lensless Compressive Imaging
We develop a lensless compressive imaging architecture, which consists of an
aperture assembly and a single sensor, without using any lens. An anytime
algorithm is proposed to reconstruct images from the compressive measurements;
the algorithm produces a sequence of solutions that monotonically converge to
the true signal (thus, anytime). The algorithm is developed based on the
sparsity of local overlapping patches (in the transformation domain) and
state-of-the-art results have been obtained. Experiments on real data
demonstrate that encouraging results are obtained by measuring about 10% (of
the image pixels) compressive measurements. The reconstruction results of the
proposed algorithm are compared with the JPEG compression (based on file sizes)
and the reconstructed image quality is close to the JPEG compression, in
particular at a high compression rate.Comment: 37 pages, 10 figures. Submitted to SIAM Journal on Imaging Scienc
Rank Minimization for Snapshot Compressive Imaging
Snapshot compressive imaging (SCI) refers to compressive imaging systems
where multiple frames are mapped into a single measurement, with video
compressive imaging and hyperspectral compressive imaging as two representative
applications. Though exciting results of high-speed videos and hyperspectral
images have been demonstrated, the poor reconstruction quality precludes SCI
from wide applications.This paper aims to boost the reconstruction quality of
SCI via exploiting the high-dimensional structure in the desired signal. We
build a joint model to integrate the nonlocal self-similarity of
video/hyperspectral frames and the rank minimization approach with the SCI
sensing process. Following this, an alternating minimization algorithm is
developed to solve this non-convex problem. We further investigate the special
structure of the sampling process in SCI to tackle the computational workload
and memory issues in SCI reconstruction. Both simulation and real data
(captured by four different SCI cameras) results demonstrate that our proposed
algorithm leads to significant improvements compared with current
state-of-the-art algorithms. We hope our results will encourage the researchers
and engineers to pursue further in compressive imaging for real applications.Comment: 18 pages, 21 figures, and 2 tables. Code available at
https://github.com/liuyang12/DeSC
Optimal Compressive Imaging of Fourier Data
Applications such as Magnetic Resonance Tomography acquire imaging data by
point samples of their Fourier transform. This raises the question of balancing
the efficiency of the sampling strategies with the approximation accuracy of an
associated reconstruction procedure. In this paper, we introduce a novel
sampling-reconstruction scheme based on a random anisotropic sampling pattern
and a compressed sensing type reconstruction strategy with a variant of
dualizable shearlet frames as sparsifying representation system. For this
scheme, we prove asymptotic optimality in an approximation theoretic sense for
cartoon-like functions as a model class for the imaging data. Finally, we
present numerical experiments showing the superiority of our scheme over other
approaches
Undersampled Phase Retrieval with Outliers
We propose a general framework for reconstructing transform-sparse images
from undersampled (squared)-magnitude data corrupted with outliers. This
framework is implemented using a multi-layered approach, combining multiple
initializations (to address the nonconvexity of the phase retrieval problem),
repeated minimization of a convex majorizer (surrogate for a nonconvex
objective function), and iterative optimization using the alternating
directions method of multipliers. Exploiting the generality of this framework,
we investigate using a Laplace measurement noise model better adapted to
outliers present in the data than the conventional Gaussian noise model. Using
simulations, we explore the sensitivity of the method to both the
regularization and penalty parameters. We include 1D Monte Carlo and 2D image
reconstruction comparisons with alternative phase retrieval algorithms. The
results suggest the proposed method, with the Laplace noise model, both
increases the likelihood of correct support recovery and reduces the mean
squared error from measurements containing outliers. We also describe exciting
extensions made possible by the generality of the proposed framework, including
regularization using analysis-form sparsity priors that are incompatible with
many existing approaches.Comment: 11 pages, 9 figure
Analog to Digital Cognitive Radio: Sampling, Detection and Hardware
The proliferation of wireless communications has recently created a
bottleneck in terms of spectrum availability. Motivated by the observation that
the root of the spectrum scarcity is not a lack of resources but an inefficient
managing that can be solved, dynamic opportunistic exploitation of spectral
bands has been considered, under the name of Cognitive Radio (CR). This
technology allows secondary users to access currently idle spectral bands by
detecting and tracking the spectrum occupancy. The CR application revisits this
traditional task with specific and severe requirements in terms of spectrum
sensing and detection performance, real-time processing, robustness to noise
and more. Unfortunately, conventional methods do not satisfy these demands for
typical signals, that often have very high Nyquist rates.
Recently, several sampling methods have been proposed that exploit signals' a
priori known structure to sample them below the Nyquist rate. Here, we review
some of these techniques and tie them to the task of spectrum sensing in the
context of CR. We then show how issues related to spectrum sensing can be
tackled in the sub-Nyquist regime. First, to cope with low signal to noise
ratios, we propose to recover second-order statistics from the low rate
samples, rather than the signal itself. In particular, we consider
cyclostationary based detection, and investigate CR networks that perform
collaborative spectrum sensing to overcome channel effects. To enhance the
efficiency of the available spectral bands detection, we present joint spectrum
sensing and direction of arrival estimation methods. Throughout this work, we
highlight the relation between theoretical algorithms and their practical
implementation. We show hardware simulations performed on a prototype we built,
demonstrating the feasibility of sub-Nyquist spectrum sensing in the context of
CR.Comment: Submitted to IEEE Signal Processing Magazin
MoDL: Model Based Deep Learning Architecture for Inverse Problems
We introduce a model-based image reconstruction framework with a convolution
neural network (CNN) based regularization prior. The proposed formulation
provides a systematic approach for deriving deep architectures for inverse
problems with the arbitrary structure. Since the forward model is explicitly
accounted for, a smaller network with fewer parameters is sufficient to capture
the image information compared to black-box deep learning approaches, thus
reducing the demand for training data and training time. Since we rely on
end-to-end training, the CNN weights are customized to the forward model, thus
offering improved performance over approaches that rely on pre-trained
denoisers. The main difference of the framework from existing end-to-end
training strategies is the sharing of the network weights across iterations and
channels. Our experiments show that the decoupling of the number of iterations
from the network complexity offered by this approach provides benefits
including lower demand for training data, reduced risk of overfitting, and
implementations with significantly reduced memory footprint. We propose to
enforce data-consistency by using numerical optimization blocks such as
conjugate gradients algorithm within the network; this approach offers faster
convergence per iteration, compared to methods that rely on proximal gradients
steps to enforce data consistency. Our experiments show that the faster
convergence translates to improved performance, especially when the available
GPU memory restricts the number of iterations.Comment: published in IEEE Transaction on Medical Imagin
Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification
Dictionary learning algorithms have been successfully used in both
reconstructive and discriminative tasks, where the input signal is represented
by a linear combination of a few dictionary atoms. While these methods are
usually developed under sparsity constrain (prior) in the input
domain, recent studies have demonstrated the advantages of sparse
representation using structured sparsity priors in the kernel domain. In this
paper, we propose a supervised dictionary learning algorithm in the kernel
domain for hyperspectral image classification. In the proposed formulation, the
dictionary and classifier are obtained jointly for optimal classification
performance. The supervised formulation is task-driven and provides learned
features from the hyperspectral data that are well suited for the
classification task. Moreover, the proposed algorithm uses a joint
() sparsity prior to enforce collaboration among the neighboring
pixels. The simulation results illustrate the efficiency of the proposed
dictionary learning algorithm.Comment: 5 pages, IEEE International Conference on Acoustics, Speech, and
Signal Processing (ICASSP), 201
Self-evolving ghost imaging
Ghost imaging can capture 2D images with a point detector instead of an array
sensor. It therefore offers a solution to the challenge of building area format
sensors in wavebands where such sensors are difficult and expensive to produce
and opens up new imaging modalities due to high-performance single-pixel
detectors. Traditionally, ghost imaging retrieves the image of an object
offline, by correlating measured light intensities and applied illuminating
patterns. Here we present a feedback-based approach for online updating of the
imaging result that can bypass post-processing, termed self-evolving ghost
imaging (SEGI). We introduce a genetic algorithm to optimize the illumination
patterns in real-time to match the objects shape according to the measured
total light intensity. We theoretically and experimentally demonstrate this
concept for static and dynamic imaging. This method opens new perspectives for
real-time ghost imaging in applications such as remote sensing (e.g. machine
vision, LiDAR systems in autonomous vehicles) and biological imaging
Medical Image Generation using Generative Adversarial Networks
Generative adversarial networks (GANs) are unsupervised Deep Learning
approach in the computer vision community which has gained significant
attention from the last few years in identifying the internal structure of
multimodal medical imaging data. The adversarial network simultaneously
generates realistic medical images and corresponding annotations, which proven
to be useful in many cases such as image augmentation, image registration,
medical image generation, image reconstruction, and image-to-image translation.
These properties bring the attention of the researcher in the field of medical
image analysis and we are witness of rapid adaption in many novel and
traditional applications. This chapter provides state-of-the-art progress in
GANs-based clinical application in medical image generation, and cross-modality
synthesis. The various framework of GANs which gained popularity in the
interpretation of medical images, such as Deep Convolutional GAN (DCGAN),
Laplacian GAN (LAPGAN), pix2pix, CycleGAN, and unsupervised image-to-image
translation model (UNIT), continue to improve their performance by
incorporating additional hybrid architecture, has been discussed. Further, some
of the recent applications of these frameworks for image reconstruction, and
synthesis, and future research directions in the area have been covered.Comment: 19 pages, 3 figures, 5 table
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