[PDF] Top 20 K-cluster-valued compressive sensing for imaging

... of K-means algorithm are repeated 100 ...the K-cluster-valued and conventional CSs to com- press one lunar image relying on a canonical (pixel) sparsity ... See full document

... each cluster corresponds to a target. Then the largest response in the cluster and its maximum neighbor are interpolated to arrive at an estimate of the location of this ... See full document

... where y ∈ R m is the acquired data from the scanner called as k-space MRI data, and x ∈ R n is the MR image rearranged to a column vector, m n. ψ ∈ R m × n represents a random undersampling matrix, and ϕ ∈ R n × n ... See full document

... the imaging quality is greatly improved by the proposed ...compare imaging quality at same imaging mode and frequency band, such as Figure 4(b) and Figure 4(c), which correspond to images ... See full document

... improve imaging speed become an urgent problem to be ...compressed sensing (CS) theory [6, 7], which is gaining increasing interest in the field of radar imaging ... See full document

... According to the ray theory view of electromagnetic wave propagation, the transmitted signal follows the path in Fig. 2. At the boundary between two different media (such as air and soil), the direction changes according ... See full document

... when K is 1 or a very small number, after reconstruction of compressive sensing, there is at most only 1 non-zero value in each range profile, like a delta function, and any interpolation does not ... See full document

... of compressive sensing (CS) in ...on compressive sensing in 2004 on magnetic resonance imaging (MRI) demonstrated the capability of CS to produce identical or near-identical images to ... See full document

... To the best of our knowledge, a general description on how to apply CS for stripmap synthetic aperture imag- ing is not addressed in the literature yet. In this paper, we use the linear system model of the received echo ... See full document

... Although compressive sensing technology can effectively re- duce the energy consumption of each node in the network, it is directly related to the measurement value M in com- pressive ...hybrid ... See full document

... The aim of this experiment is to analyze the influence of SNR on the performance of the approaches. To com- pare the noise tolerance of the four methods, we use 64 pulses corresponding to the dwell time [−0.32, 0.32] ... See full document

... the imaging results obtained by the Fourier transform and the SVD methods are also given for ...the imaging quality limitation imposed by the low number of flight tracks and their non-uniform ...the ... See full document

... The goal of this paper is to formulate a target tracking system using compressed signals. Block compressive sensing technique is used in this paper in order to compress the video frames. This system is ... See full document

... using compressive sensing. The goal of compressive sensing is to compress the speech signal at transmitter and decompress it at the receiver from far less samples than the nyquist ... See full document

... Conventionally, the signal of interest is sampled at nyquist rate and a large part of these samples is eliminated during the compression stage. This leads to unnecessary hardware and software load. Compressed ... See full document

... remote sensing images as the experimental data, introduce compressive sensing algorithm into remote sensing image fusion methods, and compare it with several common pixel-level fusion ... See full document

... cited. Compressive sensing (CS) is mainly concerned with low-coherence pairs, since the number of samples needed to recover the signal is proportional to the mutual coherence between projection matrix and ... See full document

... image fusion effects, especially in the edge processing of the fused images. With the decrease of the image com- pressive rates, the image fusion effects decrease signifi- cantly, which is consistent with the subjective ... See full document

... for compressive sensing, which can preserve some information in previous layers and also can improve the convergence rate and accelerate the training ... See full document

... Compressive sensing theory can be efficiently used in speech processing ...the Sensing matrix and Sparsity domain conversion of signal in compressive sensing, sampling, compression and ... See full document