An augmented Lagrangian approach to general dictionary learning for image denoising

Authors:
Qiegen Liu;Shanshan Wang;Jianhua Luo;Yuemin Zhu;Meng Ye
Affiliations:
Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200240, China and Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China;Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200240, China;Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200240, China and College of Aeronautics and Astronautics, Shanghai Jiaotong University, Shanghai 200240, China;CREATIS, CNRS UMR 5220, Inserm U 630, INSA Lyon, University of Lyon 1, Lyon, France;Department of Mathematics, Shanghai Jiaotong University, Shanghai 200240, China
Venue:
Journal of Visual Communication and Image Representation
Year:
2012

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Abstract

This paper presents an augmented Lagrangian (AL) based method for designing of overcomplete dictionaries for sparse representation with general l"q-data fidelity term (q=