title: Image Reconstruction Using Analysis Model Prior.
abstract: The analysis model has been previously exploited as an alternative to the classical sparse synthesis model for designing image reconstruction methods. Applying a suitable analysis operator on the image of interest yields a cosparse outcome which enables us to reconstruct the image from undersampled data. In this work, we introduce additional prior in the analysis context and theoretically study the uniqueness issues in terms of analysis operators in general position and the specific 2D finite difference operator. We establish bounds on the minimum measurement numbers which are lower than those in cases without using analysis model prior. Based on the idea of iterative cosupport detection (ICD), we develop a novel image reconstruction model and an effective algorithm, achieving significantly better reconstruction performance. Simulation results on synthetic and practical magnetic resonance (MR) images are also shown to illustrate our theoretical claims.
authors.affiliation: School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Han
authors.affiliation: School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Huiqian
authors.initials: H
authors.lastname: Du
authors.affiliation: Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
authors.forename: Fan
authors.initials: F
authors.lastname: Lam
authors.affiliation: School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Wenbo
authors.initials: W
authors.lastname: Mei
authors.affiliation: School of Mathematics, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Liping
authors.initials: L
authors.lastname: Fang
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000473:pathology; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008490:Medical Informatics / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging