a block-wise random sampling approach: compressed sensing problem
نویسندگان
چکیده
the focus of this paper is to consider the compressed sensing problem. it is stated that the compressed sensing theory, under certain conditions, helps relax the nyquist sampling theory and takes smaller samples. one of the important tasks in this theory is to carefully design measurement matrix (sampling operator). most existing methods in the literature attempt to optimize a randomly initialized matrix with the aim of decreasing the amount of required measurements. however, these approaches mainly lead to sophisticated structure of measurement matrix which makes it very difficult to implement. in this paper we propose an intermediate structure for the measurement matrix based on random sampling. the main advantage of block-based proposed technique is simplicity and yet achieving acceptable performance obtained through using conventional techniques. the experimental results clearly confirm that in spite of simplicity of the proposed approach it can be competitive to the existing methods in terms of reconstruction quality. it also outperforms existing methods in terms of computation time.
منابع مشابه
A Block-Wise random sampling approach: Compressed sensing problem
The focus of this paper is to consider the compressed sensing problem. It is stated that the compressed sensing theory, under certain conditions, helps relax the Nyquist sampling theory and takes smaller samples. One of the important tasks in this theory is to carefully design measurement matrix (sampling operator). Most existing methods in the literature attempt to optimize a randomly initiali...
متن کاملRandom Sampling and Signal Reconstruction Based on Compressed Sensing
Compressed sensing (CS) sampling is a sampling method which is based on the signal sparse. Much information can be extracted as little as possible of the data by applying CS and this method is the idea of great theoretical and applied prospects. In the framework of compressed sensing theory, the sampling rate is no longer decided in the bandwidth of the signal, but it depends on the structure a...
متن کاملIterative methods for random sampling and compressed sensing recovery
In this paper, two methods are proposed which address the random sampling and compressed sensing recovery problems. The proposed random sampling recovery method is the Iterative Method with Adaptive Thresholding and Interpolation (IMATI). Simulation results indicate that the proposed method outperforms existing random sampling recovery methods such as Iterative Method with Adaptive Thresholding...
متن کاملRandom Sampling and Signal Bregman Reconstruction Based on Compressed Sensing
Compressed sensing (CS) sampling is a sampling method which is based on the signal sparse. Much information can be extracted from as little as possible of the data by applying CS, and this method is the idea of great theoretical and applied prospects. In the framework of compressed sensing theory, the sampling rate is no longer decided in the bandwidth of the signal, but it depends on the struc...
متن کاملThe Two Stage l1 Approach to the Compressed Sensing Problem
This paper gives new results on the recovery of sparse signals using l1-norm minimization. We introduce a two-stage l1 algorithm equivalent to the first two iterations of the alternating l1 relaxation introduced in [5] for an appropriate value of the Lagrange multiplier. The first step consists of the standard l1 relaxation. The second step consists of optimizing the l1 norm of a subvector whos...
متن کاملAn Alternating l1 Approach to the Compressed Sensing Problem
Compressed sensing is a new methodology for constructing sensors which allow sparse signals to be efficiently recovered using only a small number of observations. The recovery problem can often be stated as the one of finding the solution of an underdetermined system of linear equations with the smallest possible support. The most studied relaxation of this hard combinatorial problem is the l1-...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
journal of ai and data miningناشر: shahrood university of technology
ISSN 2322-5211
دوره 3
شماره 1 2015
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023