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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.<\/jats:p>","DOI":"10.1155\/2016\/7571934","type":"journal-article","created":{"date-parts":[[2016,6,9]],"date-time":"2016-06-09T22:06:10Z","timestamp":1465509970000},"page":"1-15","source":"Crossref","is-referenced-by-count":4,"title":["Image Reconstruction Using Analysis Model Prior"],"prefix":"10.1155","volume":"2016","author":[{"given":"Yu","family":"Han","sequence":"first","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiqian","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Lam","sequence":"additional","affiliation":[{"name":"Beckman 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