Sparsity and incoherence in compressive sampling
نویسندگان
چکیده
منابع مشابه
Sparsity and incoherence in compressive sampling
We consider the problem of reconstructing a sparse signal x0 ∈ R from a limited number of linear measurements. Given m randomly selected samples of Ux0, where U is an orthonormal matrix, we show that 1 minimization recovers x0 exactly when the number of measurements exceeds m const · μ(U) · S · log n, where S is the number of nonzero components in x0 and μ is the largest entry in U properly nor...
متن کاملBeyond incoherence: stable and robust sampling strategies for compressive imaging
In many signal processing applications, one wishes to acquire images that are sparse in transform domains such as spatial finite differences or wavelets using frequency domain samples. For such applications, overwhelming empirical evidence suggests that superior image reconstruction can be obtained through variable density sampling strategies that concentrate on lower frequencies. The wavelet a...
متن کاملRank-Sparsity Incoherence for Matrix Decomposition
Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-rank components. Such a problem arises in a number of applications in model and system identification, and is NP-hard in general. In this paper we consider a convex optimization formulation to splitting the specified mat...
متن کاملEstimation of block sparsity in compressive sensing
Explicitly using the block structure of the unknown signal can achieve better recovery performance in compressive censing. An unknown signal with block structure can be accurately recovered from underdetermined linear measurements provided that it is sufficiently block sparse. However, in practice, the block sparsity level is typically unknown. In this paper, we consider a soft measure of block...
متن کاملCompressive Sampling based Single-Image Super-resolution Reconstruction by dual-sparsity and Non-local Similarity Regularizer
0167-8655/$ see front matter 2012 Elsevier B.V. A doi:10.1016/j.patrec.2012.02.006 ⇑ Corresponding author. Tel.: +86 029 88204298; fa E-mail address: [email protected] (S. Yang). Recent development on Compressive Sampling (or compressive sensing, CS) theory suggests that HighResolution (HR) images can be correctly recovered from their Low-Resolution (LR) version under mild conditions. Inspir...
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: Inverse Problems
سال: 2007
ISSN: 0266-5611,1361-6420
DOI: 10.1088/0266-5611/23/3/008