12,956 research outputs found
Multi-Scale Deep Compressive Sensing Network
With joint learning of sampling and recovery, the deep learning-based
compressive sensing (DCS) has shown significant improvement in performance and
running time reduction. Its reconstructed image, however, losses high-frequency
content especially at low subrates. This happens similarly in the multi-scale
sampling scheme which also samples more low-frequency components. In this
paper, we propose a multi-scale DCS convolutional neural network (MS-DCSNet) in
which we convert image signal using multiple scale-based wavelet transform,
then capture it through convolution block by block across scales. The initial
reconstructed image is directly recovered from multi-scale measurements.
Multi-scale wavelet convolution is utilized to enhance the final reconstruction
quality. The network is able to learn both multi-scale sampling and multi-scale
reconstruction, thus results in better reconstruction quality.Comment: 4 pages, 4 figures, 2 tables, IEEE International Conference on Visual
Communication and Image Processing (VCIP
Image Classification with A Deep Network Model based on Compressive Sensing
To simplify the parameter of the deep learning network, a cascaded
compressive sensing model "CSNet" is implemented for image classification.
Firstly, we use cascaded compressive sensing network to learn feature from the
data. Secondly, CSNet generates the feature by binary hashing and block-wise
histograms. Finally, a linear SVM classifier is used to classify these
features. The experiments on the MNIST dataset indicate that higher
classification accuracy can be obtained by this algorithm
A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks
Purpose: Neural networks have received recent interest for reconstruction of
undersampled MR acquisitions. Ideally network performance should be optimized
by drawing the training and testing data from the same domain. In practice,
however, large datasets comprising hundreds of subjects scanned under a common
protocol are rare. The goal of this study is to introduce a transfer-learning
approach to address the problem of data scarcity in training deep networks for
accelerated MRI.
Methods: Neural networks were trained on thousands of samples from public
datasets of either natural images or brain MR images. The networks were then
fine-tuned using only few tens of brain MR images in a distinct testing domain.
Domain-transferred networks were compared to networks trained directly in the
testing domain. Network performance was evaluated for varying acceleration
factors (2-10), number of training samples (0.5-4k) and number of fine-tuning
samples (0-100).
Results: The proposed approach achieves successful domain transfer between MR
images acquired with different contrasts (T1- and T2-weighted images), and
between natural and MR images (ImageNet and T1- or T2-weighted images).
Networks obtained via transfer-learning using only tens of images in the
testing domain achieve nearly identical performance to networks trained
directly in the testing domain using thousands of images.
Conclusion: The proposed approach might facilitate the use of neural networks
for MRI reconstruction without the need for collection of extensive imaging
datasets
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Linear encoding of sparse vectors is widely popular, but is commonly
data-independent -- missing any possible extra (but a priori unknown) structure
beyond sparsity. In this paper we present a new method to learn linear encoders
that adapt to data, while still performing well with the widely used
decoder. The convex decoder prevents gradient propagation as needed in
standard gradient-based training. Our method is based on the insight that
unrolling the convex decoder into projected subgradient steps can address
this issue. Our method can be seen as a data-driven way to learn a compressed
sensing measurement matrix. We compare the empirical performance of 10
algorithms over 6 sparse datasets (3 synthetic and 3 real). Our experiments
show that there is indeed additional structure beyond sparsity in the real
datasets; our method is able to discover it and exploit it to create excellent
reconstructions with fewer measurements (by a factor of 1.1-3x) compared to the
previous state-of-the-art methods. We illustrate an application of our method
in learning label embeddings for extreme multi-label classification, and
empirically show that our method is able to match or outperform the precision
scores of SLEEC, which is one of the state-of-the-art embedding-based
approaches.Comment: 17 pages, 7 tables, 8 figures, published in ICML 2019; part of this
work was done while Shanshan was an intern at Google Research, New Yor
Deep Learning Enabled Real Time Speckle Recognition and Hyperspectral Imaging using a Multimode Fiber Array
We demonstrate the use of deep learning for fast spectral deconstruction of
speckle patterns. The artificial neural network can be effectively trained
using numerically constructed multispectral datasets taken from a measured
spectral transmission matrix. Optimized neural networks trained on these
datasets achieve reliable reconstruction of both discrete and continuous
spectra from a monochromatic camera image. Deep learning is compared to
analytical inversion methods as well as to a compressive sensing algorithm and
shows favourable characteristics both in the oversampling and in the sparse
undersampling (compressive) regimes. The deep learning approach offers
significant advantages in robustness to drift or noise and in reconstruction
speed. In a proof-of-principle demonstrator we achieve real time recovery of
hyperspectral information using a multi-core, multi-mode fiber array as a
random scattering medium.Comment: 12 pages, 6 figures + Appendix of 5 pages and 5 figure
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI
Compressive sensing (CS) is an effective approach for fast Magnetic Resonance
Imaging (MRI). It aims at reconstructing MR images from a small number of
under-sampled data in k-space, and accelerating the data acquisition in MRI. To
improve the current MRI system in reconstruction accuracy and speed, in this
paper, we propose two novel deep architectures, dubbed ADMM-Nets in basic and
generalized versions. ADMM-Nets are defined over data flow graphs, which are
derived from the iterative procedures in Alternating Direction Method of
Multipliers (ADMM) algorithm for optimizing a general CS-based MRI model. They
take the sampled k-space data as inputs and output reconstructed MR images.
Moreover, we extend our network to cope with complex-valued MR images. In the
training phase, all parameters of the nets, e.g., transforms, shrinkage
functions, etc., are discriminatively trained end-to-end. In the testing phase,
they have computational overhead similar to ADMM algorithm but use optimized
parameters learned from the data for CS-based reconstruction task. We
investigate different configurations in network structures and conduct
extensive experiments on MR image reconstruction under different sampling
rates. Due to the combination of the advantages in model-based approach and
deep learning approach, the ADMM-Nets achieve state-of-the-art reconstruction
accuracies with fast computational speed
Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning
Employing large intelligent surfaces (LISs) is a promising solution for
improving the coverage and rate of future wireless systems. These surfaces
comprise a massive number of nearly-passive elements that interact with the
incident signals, for example by reflecting them, in a smart way that improves
the wireless system performance. Prior work focused on the design of the LIS
reflection matrices assuming full knowledge of the channels. Estimating these
channels at the LIS, however, is a key challenging problem, and is associated
with large training overhead given the massive number of LIS elements. This
paper proposes efficient solutions for these problems by leveraging tools from
compressive sensing and deep learning. First, a novel LIS architecture based on
sparse channel sensors is proposed. In this architecture, all the LIS elements
are passive except for a few elements that are active (connected to the
baseband of the LIS controller). We then develop two solutions that design the
LIS reflection matrices with negligible training overhead. In the first
approach, we leverage compressive sensing tools to construct the channels at
all the LIS elements from the channels seen only at the active elements. These
full channels can then be used to design the LIS reflection matrices with no
training overhead. In the second approach, we develop a deep learning based
solution where the LIS learns how to optimally interact with the incident
signal given the channels at the active elements, which represent the current
state of the environment and transmitter/receiver locations. We show that the
achievable rates of the proposed compressive sensing and deep learning
solutions approach the upper bound, that assumes perfect channel knowledge,
with negligible training overhead and with less than 1% of the elements being
active.Comment: Submitted to IEEE Access. The code will be available soo
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration
Low-rank signal modeling has been widely leveraged to capture non-local
correlation in image processing applications. We propose a new method that
employs low-rank tensor factor analysis for tensors generated by grouped image
patches. The low-rank tensors are fed into the alternative direction multiplier
method (ADMM) to further improve image reconstruction. The motivating
application is compressive sensing (CS), and a deep convolutional architecture
is adopted to approximate the expensive matrix inversion in CS applications. An
iterative algorithm based on this low-rank tensor factorization strategy,
called NLR-TFA, is presented in detail. Experimental results on noiseless and
noisy CS measurements demonstrate the superiority of the proposed approach,
especially at low CS sampling rates
Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks
Convolutional Neural Networks (CNN) based image reconstruction methods have
been intensely used for X-ray computed tomography (CT) reconstruction
applications. Despite great success, good performance of this data-based
approach critically relies on a representative big training data set and a
dense convoluted deep network. The indiscriminating convolution connections
over all dense layers could be prone to over-fitting, where sampling biases are
wrongly integrated as features for the reconstruction. In this paper, we report
a robust hierarchical synthesis reconstruction approach, where training data is
pre-processed to separate the information on the domains where sampling biases
are suspected. These split bands are then trained separately and combined
successively through a hierarchical synthesis network. We apply the
hierarchical synthesis reconstruction for two important and classical
tomography reconstruction scenarios: the spares-view reconstruction and the
phase reconstruction. Our simulated and experimental results show that
comparable or improved performances are achieved with a dramatic reduction of
network complexity and computational cost. This method can be generalized to a
wide range of applications including material characterization, in-vivo
monitoring and dynamic 4D imaging.Comment: 9 pages, 6 figures, 2 table
Deep Compressive Autoencoder for Action Potential Compression in Large-Scale Neural Recording
Understanding the coordinated activity underlying brain computations requires
large-scale, simultaneous recordings from distributed neuronal structures at a
cellular-level resolution. One major hurdle to design high-bandwidth,
high-precision, large-scale neural interfaces lies in the formidable data
streams that are generated by the recorder chip and need to be online
transferred to a remote computer. The data rates can require hundreds to
thousands of I/O pads on the recorder chip and power consumption on the order
of Watts for data streaming alone. We developed a deep learning-based
compression model to reduce the data rate of multichannel action potentials.
The proposed model is built upon a deep compressive autoencoder (CAE) with
discrete latent embeddings. The encoder is equipped with residual
transformations to extract representative features from spikes, which are
mapped into the latent embedding space and updated via vector quantization
(VQ). The decoder network reconstructs spike waveforms from the quantized
latent embeddings. Experimental results show that the proposed model
consistently outperforms conventional methods by achieving much higher
compression ratios (20-500x) and better or comparable reconstruction
accuracies. Testing results also indicate that CAE is robust against a diverse
range of imperfections, such as waveform variation and spike misalignment, and
has minor influence on spike sorting accuracy. Furthermore, we have estimated
the hardware cost and real-time performance of CAE and shown that it could
support thousands of recording channels simultaneously without excessive
power/heat dissipation. The proposed model can reduce the required data
transmission bandwidth in large-scale recording experiments and maintain good
signal qualities. The code of this work has been made available at
https://github.com/tong-wu-umn/spike-compression-autoencoderComment: 19 pages, 13 figure
- …