Understanding compressive sensing and sparse representation-based super-resolution

Naveen Kulkarni, Pradeep Nagesh, Rahul Gowda, Baoxin Li

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


Recently, compressive sensing (CS) has emerged as a powerful tool for solving a class of inverse/underdetermined problems in computer vision and image processing. In this paper, we investigate the application of CS paradigms on single image super-resolution (SR) problems that are considered to be the most challenging in this class. In light of recent promising results, we propose novel tools for analyzing sparse representation-based inverse problems using redundant dictionary basis. Further, we provide novel results establishing tighter correspondence between SR and CS. As such, we gain insights into questions concerning regularizing the solution to the underdetermined problem, such as follows. 1) Is sparsity prior alone sufficient? 2) What is a good dictionary? 3) What is the practical implication of noncompliance with theoretical CS hypothesis? Unlike in other underdetermined problems that assume random down-projections, the low-resolution image formation model employed in CS-based SR is a deterministic down-projection that may not necessarily satisfy some critical assumptions of CS. We further investigate the impact of such projections in concern to the above questions.

Original languageEnglish (US)
Article number6111276
Pages (from-to)778-789
Number of pages12
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number5
StatePublished - 2012


  • Compressive sensing
  • Grammian measures
  • redundant dictionary
  • sparse representation
  • super-resolution

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering


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