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Super Resolution based on Compressed Sensing (CS) considers low resolution (LR) image patch as the compressive measurement of its corresponding high resolution (HR) patch. In this paper we propose a single image super resolution scheme with compressive K-SVD algorithm(CKSVD) for dictionary learning incorporating fusion of sparse approximation algorithms to produce better results. The CKSVD algo...

Compressive sampling is an emerging technique that promises to effectively recover a sparse signal from far fewer measurements than its dimension. The compressive sampling theory assures almost an exact recovery of a sparse signal if the signal is sensed randomly where the number of the measurements taken is proportional to the sparsity level and a log factor of the signal dimension. Encouraged...

In this note, we prove several of the main results of compressive sensing from the spherical section property. 1 Compressive sensing In the past four years, there has been extensive activity on compressive sensing. Recently, Kashin and Temlyakov [7] and Zhang [8] have developed simplified proofs of some of the main theorems of compressive sensing using the spherical section property. This notes...

This paper demonstrates that convolution with random waveform followed by random time-domain subsampling is a universally efficient compressive sensing strategy. We show that an n-dimensional signal which is S-sparse in any fixed orthonormal representation can be recovered from m & S log n samples from its convolution with a pulse whose Fourier transform has unit magnitude and random phase at a...

Compressive sensing (sampling) is a novel technology and science domain that exploits the option to sample radiometric and spectroscopic signals at a lower sampling rate than the one dictated by the traditional theory of ideal sampling. In the paper some general concepts and characteristics regarding the use of compressive sampling in instruments devoted to Earth observation is discussed. The r...

Huge data processing contributes many factors in wireless sensor network such as network traffic and energy constraint. Using compressive sensing a new technique in data acquisition which reduced the required sampling rate to reconstruct the original signal will therefore lessen the power consumption. This paper will implement the compressive sensing algorithm of the wireless sensor network ins...

Compressive sensing is a new field in signal processing and applied mathematics. It allows one to simultaneously sample and compress signals which are known to have a sparse representation in a known basis or dictionary along with the subsequent recovery by linear programming (requiring polynomial (P) time) of the original signals with low or no error [1, 2, 3]. Compressive measurements or samp...

In this research, two radiofrequency identification (RFID) antenna sensor designs are tested for compressive strain measurement. The first design is a passive (battery-free) folded patch antenna sensor with a planar dimension of 61mm × 69mm. The second design is a slotted patch antenna sensor, whose dimension is reduced to 48mm × 44mm by introducing slots on antenna conducting layer to detour s...

This paper designs and evaluates a variant of CoSaMP algorithm, for recovering the sparse signal s from the compressive measurement ( ) v Uw s   given a fixed lowrank subspace spanned by U. Instead of firstly recovering the full vector then separating the sparse part from the structured dense part, the proposed algorithm directly works on the compressive measurement to do the separation. We i...