Compressed sensing (CS) has great potential for use in video data acquisition and storage because it makes it unnecessary to collect an enormous amount of data and to perform the computationally demanding compression process. We propose an effective CS algorithm for video that consists of two iterative stages. In the first stage, frames containing the dominant structure are estimated. These frames are obtained by thresholding the coefficients of similar blocks. In the second stage, refined residual frames are reconstructed from the original measurements and the measurements corresponding to the frames estimated in the first stage. These two stages are iterated until convergence. The proposed algorithm exhibits superior subjective image quality and significantly improves the peak-signal-to-noise ratio and the structural similarity index measure compared to other state-of-the-art CS algorithms.
In this paper, compressive sensing strategies for interception of Frequency-Hopping Spread Spectrum (FHSS)
signals are introduced. Rapid switching of the carrier among many frequency channels using a pseudorandom
sequence (unknown to the eavesdropper) makes FHSS signals dicult to intercept. The conventional approach to
intercept FHSS signals necessitates capturing of all frequency channels and, thus, requires the Analog-to-Digital
Converters (ADCs) to sample at very high rates. Using the fact that the FHSS signals have sparse instanta-
neous spectra, we propose compressive sensing strategies for their interception. The proposed techniques are
validated using Gaussian Frequency-Shift Keying (GFSK) modulated FHSS signals as dened by the Bluetooth
The recently introduced Compressed Sensing (CS) theory explains how sparse or compressible signals can be
reconstructed from far fewer samples than what was previously believed possible. The CS theory has attracted
significant attention for applications such as Magnetic Resonance Imaging (MRI) where long acquisition times have
been problematic. This is especially true for dynamic MRI applications where high spatio-temporal resolution is needed.
For example, in cardiac cine MRI, it is desirable to acquire the whole cardiac volume within a single breath-hold in order
to avoid artifacts due to respiratory motion. Conventional MRI techniques do not allow reconstruction of high resolution
image sequences from such limited amount of data.
Vaswani et al. recently proposed an extension of the CS framework to problems with partially known support (i.e.
sparsity pattern). In their work, the problem of recursive reconstruction of time sequences of sparse signals was
considered. Under the assumption that the support of the signal changes slowly over time, they proposed using the
support of the previous frame as the "known" part of the support for the current frame. While this approach works well
for image sequences with little or no motion, motion causes significant change in support between adjacent frames. In
this paper, we illustrate how motion estimation and compensation techniques can be used to reconstruct more accurate
estimates of support for image sequences with substantial motion (such as cardiac MRI). Experimental results using
phantoms as well as real MRI data sets illustrate the improved performance of the proposed technique.
Proc. SPIE. 7073, Applications of Digital Image Processing XXXI
KEYWORDS: Magnetic resonance imaging, Image restoration, Fourier transforms, Receivers, Data acquisition, Photonic integrated circuits, In vivo imaging, Detection theory, Alternate lighting of surfaces, Compressed sensing
Although magnetic resonance imaging (MRI) is routinely used in clinical practice, long acquisition times limit its practical utility in many applications. To increase the data acquisition speed of MRI, parallel MRI (pMRI) techniques have recently been proposed. These techniques utilize multi-channel receiver arrays and are based on simultaneous acquisition of data from multiple receiver coils. Recently, a novel framework called Compressed Sensing (CS) was introduced. Since this new framework illustrates how signals can be
reconstructed from much fewer samples than suggested by the Nyquist theory, it has the potential to significantly accelerate data acquisition in MRI. This paper illustrates that CS and pMRI techniques can be combined and such joint processing yields results that are superior to those obtained from independent utilization of each technique.