منابع مشابه
Compression2: compressed sensing with compressed coil arrays
Background Imaging with large coil arrays is desirable for rapid imaging and high signal to noise ratio. Compressed sensing (CS) is a promising way to accelerate myocardial perfusion imaging [1]. However with increasing number of coils CS is costly in terms of memory and computation time. Coil compression methods for reconstructing cardiac cine data with parallel imaging have been proposed [2,3...
متن کامل"Compressed" compressed sensing
The field of compressed sensing has shown that a sparse but otherwise arbitrary vector can be recovered exactly from a small number of randomly constructed linear projections (or samples). The question addressed in this paper is whether an even smaller number of samples is sufficient when there exists prior knowledge about the distribution of the unknown vector, or when only partial recovery is...
متن کاملFrames for compressed sensing using coherence
We give some new results on sparse signal recovery in the presence of noise, for weighted spaces. Traditionally, were used dictionaries that have the norm equal to 1, but, for random dictionaries this condition is rarely satised. Moreover, we give better estimations then the ones given recently by Cai, Wang and Xu.
متن کاملCompressed sensing with corrupted observations
We proposed a weighted l minimization: min , ‖x‖ + λ‖f‖ s.t.Ax+ f= b to recover a sparse vector x and the corrupted noise vector f from a linear measurement b = Ax + f when the sensing matrix A is an m × n row i.i.d subgaussian matrix. Our first result shows that the recovery is possible when the fraction of corrupted noise is smaller than a positive constant, provided that ‖x‖ ≤ O(n/ln (n/‖x ∗...
متن کاملCompressed Sensing with Nonlinear Observations
Compressed sensing is a recently developed signal acquisition technique. In contrast to traditional sampling methods, significantly fewer samples are required whenever the signals admit a sparse representation. Crucially, sampling methods can be constructed that allow the reconstruction of sparse signals from a small number of measurements using efficient algorithms. We have recently generalise...
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: BMC Neuroscience
سال: 2011
ISSN: 1471-2202
DOI: 10.1186/1471-2202-12-s1-p251