137,180 research outputs found
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
Inspired by recent advances in deep learning, we propose a framework for
reconstructing MR images from undersampled data using a deep cascade of
convolutional neural networks to accelerate the data acquisition process. We
show that for Cartesian undersampling of 2D cardiac MR images, the proposed
method outperforms the state-of-the-art compressed sensing approaches, such as
dictionary learning-based MRI (DLMRI) reconstruction, in terms of
reconstruction error, perceptual quality and reconstruction speed for both
3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the
method proposed is approximately twice as small, allowing to preserve
anatomical structures more faithfully. Using our method, each image can be
reconstructed in 23 ms, which is fast enough to enable real-time applications
From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals
Conventional sub-Nyquist sampling methods for analog signals exploit prior
information about the spectral support. In this paper, we consider the
challenging problem of blind sub-Nyquist sampling of multiband signals, whose
unknown frequency support occupies only a small portion of a wide spectrum. Our
primary design goals are efficient hardware implementation and low
computational load on the supporting digital processing. We propose a system,
named the modulated wideband converter, which first multiplies the analog
signal by a bank of periodic waveforms. The product is then lowpass filtered
and sampled uniformly at a low rate, which is orders of magnitude smaller than
Nyquist. Perfect recovery from the proposed samples is achieved under certain
necessary and sufficient conditions. We also develop a digital architecture,
which allows either reconstruction of the analog input, or processing of any
band of interest at a low rate, that is, without interpolating to the high
Nyquist rate. Numerical simulations demonstrate many engineering aspects:
robustness to noise and mismodeling, potential hardware simplifications,
realtime performance for signals with time-varying support and stability to
quantization effects. We compare our system with two previous approaches:
periodic nonuniform sampling, which is bandwidth limited by existing hardware
devices, and the random demodulator, which is restricted to discrete multitone
signals and has a high computational load. In the broader context of Nyquist
sampling, our scheme has the potential to break through the bandwidth barrier
of state-of-the-art analog conversion technologies such as interleaved
converters.Comment: 17 pages, 12 figures, to appear in IEEE Journal of Selected Topics in
Signal Processing, the special issue on Compressed Sensin
De/construction sites: Romans and the digital playground
The Roman world as attested to archaeologically and as interacted with today has its expression in a great many computational and other media. The place of visualisation within this has been paramount. This paper argues that the process of digitally constructing the Roman world and the exploration of the resultant models are useful methods for interpretation and influential factors in the creation of a popular Roman aesthetic. Furthermore, it suggests ways in which novel computational techniques enable the systematic deconstruction of such models, in turn re-purposing the many extant representations of Roman architecture and material culture
Spectral pre-modulation of training examples enhances the spatial resolution of the Phase Extraction Neural Network (PhENN)
The Phase Extraction Neural Network (PhENN) is a computational architecture,
based on deep machine learning, for lens-less quantitative phase retrieval from
raw intensity data. PhENN is a deep convolutional neural network trained
through examples consisting of pairs of true phase objects and their
corresponding intensity diffraction patterns; thereafter, given a test raw
intensity pattern PhENN is capable of reconstructing the original phase object
robustly, in many cases even for objects outside the database where the
training examples were drawn from. Here, we show that the spatial frequency
content of the training examples is an important factor limiting PhENN's
spatial frequency response. For example, if the training database is relatively
sparse in high spatial frequencies, as most natural scenes are, PhENN's ability
to resolve fine spatial features in test patterns will be correspondingly
limited. To combat this issue, we propose "flattening" the power spectral
density of the training examples before presenting them to PhENN. For phase
objects following the statistics of natural scenes, we demonstrate
experimentally that the spectral pre-modulation method enhances the spatial
resolution of PhENN by a factor of 2.Comment: 12 pages, 10 figure
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps
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