Compressed Sensing With Prior Information: Information-Theoretic Limits and Practical Decoders

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Compressed Sensing with Prior Information: Optimal Strategies, Geometry, and Bounds

We address the problem of compressed sensing (CS) with prior information: reconstruct a target CS signal with the aid of a similar signal that is known beforehand, our prior information. We integrate the additional knowledge of the similar signal into CS via l1-l1 and l1-l2 minimization. We then establish bounds on the number of measurements required by these problems to successfully reconstruc...

متن کامل

An Information-Theoretic Approach to Distributed Compressed Sensing

Compressed sensing is an emerging field based on the revelation that a small group of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multi-signal ensembles that exploit both intraand inter-signal correlation structures. The DC...

متن کامل

From compressed sensing to compressed bit-streams: practical encoders, tractable decoders

Compressed sensing is now established as an effective method for dimension reduction when the underlying signals are sparse or compressible with respect to some suitable basis or frame. One important, yet under-addressed problem regarding the compressive acquisition of analog signals is how to perform quantization. This is directly related to the important issues of how “compressed” compressed ...

متن کامل

INFORMATION THEORY TUTORIAL Compressed sensing

Signal recovery is a very practical and useful concept in both signal processing and communication area. Basically in compressed sensing, we are interested in compressing a signal, which is sparse in some domain and then, construct the original signal from the compressed one by convex optimization. This is very important to collect as less as measurements from the original signal while having t...

متن کامل

An Information Theoretic Study for Noisy Compressed Sensing With Joint Sparsity Model-2

1  Abstract—In this paper, we study a support set reconstruction problem in which the signals of interest are jointly sparse with a common support set, and sampled by joint sparsity model-2 (JSM-2) in the presence of noise. Using mathematical tools, we develop upper and lower bounds on the failure probability of support set reconstruction in terms of the sparsity, the ambient dimension, the mi...

متن کامل

ذخیره 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 Transactions on Signal Processing

سال: 2013

ISSN: 1053-587X,1941-0476

DOI: 10.1109/tsp.2012.2225051