This paper presented a new image fusion based on compressed sensing (CS). The method decomposes two or more original images using directionlet transform, gets the sparse matrix by the directionlet coefficient sparse representation, and fuses the sparse matrices with the coefficient absolute value maximum scheme. The compressed sample can be obtained through random observation. The fused image i...

Compressed Sensing (CS) is a relatively new approach to signal acquisition which has as its goal to minimize the number of measurements needed of the signal in order to guarantee that it is captured to a prescribed accuracy. It is natural to inquire whether this new subject has a role to play in Electron Microscopy (EM). In this paper, we shall describe the foundations of Compressed Sensing and...

Compressed sensing (CS) has renewed interest in sparse channel estimation. Herein, a semi-blind, iterative, sparse channel estimation method is proposed. The new method is based on minimizing Csiszár’s I-divergence using Schulz & Snyder’s iterative deautocorrelation algorithm. First, it is shown that the desired methods can be adapted to the problem of interest. The proposed semi-blind method a...

Compressed Sensing (CS) is a novel sampling paradigm that tries to take data-compression concepts down to the sampling layer of a sensory system. It states that discrete compressible signals are recoverable from sub-sampled data, when the data vector is acquired by a special linear transform of the original discrete signal vector. Distributed sampling problems especially in Wireless Sensor Netw...

IEEE SIGNAL PROCESSING MAGAZINE [72] MARCH 2008 1053-5888/08/$25.00©2008IEEE C ompressed sensing (CS) aims to reconstruct signals and images from significantly fewer measurements than were traditionally thought necessary. Magnetic resonance imaging (MRI) is an essential medical imaging tool with an inherently slow data acquisition process. Applying CS to MRI offers potentially significant scan ...

IEEE SIGNAL PROCESSING MAGAZINE [72] MARCH 2008 1053-5888/08/$25.00©2008IEEE C ompressed sensing (CS) aims to reconstruct signals and images from significantly fewer measurements than were traditionally thought necessary. Magnetic resonance imaging (MRI) is an essential medical imaging tool with an inherently slow data acquisition process. Applying CS to MRI offers potentially significant scan ...

Recently, Compressed Sensing has been proposed as a promising physical layer technique for Multi-User Detection in Massive Machine Communication (MMC). MMC is characterized by low data rates, low control signaling overhead and different traffic models compared to human-oriented communication. In this context, Compressed Sensing based Multi-User Detection (CS-MUD) enables efficient direct random...

Compressed sensing (CS) demonstrates that a sparse, or compressible signal can be acquired using a low rate acquisition process below the Nyquist rate, which projects the signal onto a small set of vectors incoherent with the sparsity basis. In this paper, we propose a new framework for compressed sensing recovery problem using iterative approximation method via 0  minimization. Instead of dir...

The newly emerging theory of compressed sensing (CS) enables restore of a sparse signal from inadequate number of linear projections. Based on compressed sensing theory, a new strategy of high-resolution range profiling of stepped-frequency (SF) radar with missing pulses is proposed. If a certain part of received pulses are missing, we demonstrate the proposed method is capable of reconstructin...

Antenna 3D pattern measurement can be a tedious and time consuming task even for antennas with manageable sizes inside anechoic chambers. Performing onsite measurements by scanning the whole 4π [sr] solid angle around the antenna under test (AUT) is more complicated. In this paper, with the aim of minimum duration of flight, a test scenario using unmanned aerial vehicles (UAV) is proposed. A pr...