Title: A novel sensing matrix for cluster structured sparse signals
Abstract:In Compressive Sensing (CS) technique, the original sparse signal is compressed in an adequate manner so as to ease its recovery from a reduced number of measurements. This depends potently on the sen...In Compressive Sensing (CS) technique, the original sparse signal is compressed in an adequate manner so as to ease its recovery from a reduced number of measurements. This depends potently on the sensing matrix. In this paper, we consider cluster structured sparse signals, and propose an enhanced Bernoulli sensing matrix. We show that the original data can be efficiently reconstructed by performing traditional signal recovery algorithms with the proposed sensing matrix. Moreover, the use of the new sensing matrix provides a considerable gain in terms of the rate of exact reconstruction.Read More
Publication Year: 2017
Publication Date: 2017-06-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 2
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot