9,006 research outputs found
Single-shot compressed ultrafast photography: a review
Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields
Inferring Sparsity: Compressed Sensing using Generalized Restricted Boltzmann Machines
In this work, we consider compressed sensing reconstruction from
measurements of -sparse structured signals which do not possess a writable
correlation model. Assuming that a generative statistical model, such as a
Boltzmann machine, can be trained in an unsupervised manner on example signals,
we demonstrate how this signal model can be used within a Bayesian framework of
signal reconstruction. By deriving a message-passing inference for general
distribution restricted Boltzmann machines, we are able to integrate these
inferred signal models into approximate message passing for compressed sensing
reconstruction. Finally, we show for the MNIST dataset that this approach can
be very effective, even for .Comment: IEEE Information Theory Workshop, 201
Deep learning versus -minimization for compressed sensing photoacoustic tomography
We investigate compressed sensing (CS) techniques for reducing the number of
measurements in photoacoustic tomography (PAT). High resolution imaging from CS
data requires particular image reconstruction algorithms. The most established
reconstruction techniques for that purpose use sparsity and
-minimization. Recently, deep learning appeared as a new paradigm for
CS and other inverse problems. In this paper, we compare a recently invented
joint -minimization algorithm with two deep learning methods, namely a
residual network and an approximate nullspace network. We present numerical
results showing that all developed techniques perform well for deterministic
sparse measurements as well as for random Bernoulli measurements. For the
deterministic sampling, deep learning shows more accurate results, whereas for
Bernoulli measurements the -minimization algorithm performs best.
Comparing the implemented deep learning approaches, we show that the nullspace
network uniformly outperforms the residual network in terms of the mean squared
error (MSE).Comment: This work has been presented at the Joint Photoacoustics Session with
the 2018 IEEE International Ultrasonics Symposium Kobe, October 22-25, 201
Model based learning for accelerated, limited-view 3D photoacoustic tomography
Recent advances in deep learning for tomographic reconstructions have shown
great potential to create accurate and high quality images with a considerable
speed-up. In this work we present a deep neural network that is specifically
designed to provide high resolution 3D images from restricted photoacoustic
measurements. The network is designed to represent an iterative scheme and
incorporates gradient information of the data fit to compensate for limited
view artefacts. Due to the high complexity of the photoacoustic forward
operator, we separate training and computation of the gradient information. A
suitable prior for the desired image structures is learned as part of the
training. The resulting network is trained and tested on a set of segmented
vessels from lung CT scans and then applied to in-vivo photoacoustic
measurement data
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