Computer Science – Information Theory
Scientific paper
2012-02-09
Computer Science
Information Theory
Scientific paper
Over the past years, there are increasing interests in recovering the signals from undersampling data where such signals are sparse under some orthogonal dictionary or tight framework, which is referred to be sparse synthetic model. More recently, its counterpart, i.e., the sparse analysis model, has also attracted researcher's attentions where many practical signals which are sparse in the truly redundant dictionary are concerned. This short paper presents important complement to the results in existing literatures for treating sparse analysis model. Firstly, we give the natural generalization of well-known restricted isometry property (RIP) to deal with sparse analysis model, where the truly arbitrary incoherent dictionary is considered. Secondly, we studied the theoretical guarantee for the accurate recovery of signal which is sparse in general redundant dictionaries through solving l1-norm sparsity-promoted optimization problem. This work shows not only that compressed sensing is viable in the context of sparse analysis, but also that accurate recovery is possible via solving l1-minimization problem.
No associations
LandOfFree
Note on RIP-based Co-sparse Analysis does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Note on RIP-based Co-sparse Analysis, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Note on RIP-based Co-sparse Analysis will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-157586