منابع مشابه
Noise Folding based on General Complete Perturbation in Compressed Sensing
This paper first present a new general completely perturbed compressed sensing (CS) model y=(A+E)(x+u)+e,called noise folding based on general completely perturbed CS system, where y ∈ Rm, u ∈ Rm, u 6= 0, e ∈ Rm, A ∈ Rm×n, m ≪ n, E ∈ Rm×n with incorporating general nonzero perturbation E to sensing matrix A and noise u into signal x simultaneously based on the standard CS model y=Ax+e. Our cons...
متن کاملNoise reduction through compressed sensing
We present an exemplar-based method for noise reduction using missing data imputation: A noise-corrupted word is sparsely represented in an over-complete basis of exemplar (clean) speech signals using only the uncorrupted time-frequency ele ments of the word. Prior to recognition the parts of the spectro gram dominated by noise are replaced by clean speech estimates obtained by projecting the...
متن کاملNoise Resilient Recovery Algorithm for Compressed Sensing
In this article, we discuss a novel greedy algorithm for the recovery of compressive sampled signals under noisy conditions. Most of the greedy recovery algorithms proposed in the literature require sparsity of the signal to be known or they estimate sparsity, for a known representation basis, from the number of measurements. These algorithms recover signals when noise level is significantly lo...
متن کاملTHE NOISE-SENSITIVITY PHASE TRANSITION IN COMPRESSED SENSING By
Consider the noisy underdetermined system of linear equations: y = Ax + z, with n × N measurement matrix A, n < N , and Gaussian white noise z ∼ N(0, σI). Both y and A are known, both x and z are unknown, and we seek an approximation to x. When x has few nonzeros, useful approximations are often obtained by `1-penalized `2 minimization, in which the reconstruction x̂ solves min ‖y −Ax‖2/2 + λ‖x‖...
متن کاملRecovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing
The typical scenario that arises in most “big data” problems is one where the ambient dimension of the signal is very large (e.g. high resolution images, gene expression data from a DNA microarray, social network data, etc.), yet is such that its desired properties lie in some low dimensional structure (sparsity, low-rankness, clusters, etc.). In the modern viewpoint, the goal is to come up wit...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Signal Processing Letters
سال: 2011
ISSN: 1070-9908,1558-2361
DOI: 10.1109/lsp.2011.2159837