- Title
- Sparse sequence recovery via a maximum a posteriori estimation
- Creator
- Hyder, Md Mashud; Mahata, Kaushik
- Relation
- 2013 IEEE International Conference on Image Processing, ICIP 2013. Proceedings of the 2013 IEEE International Conference on Image Processing, ICIP 2013 (Melbourne, VIC 15-18 September, 2013) p. 489-493
- Publisher Link
- http://dx.doi.org/10.1109/ICIP.2013.6738101
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2013
- Description
- A maximum a posteriori (MAP) estimation algorithm is given for reconstructing sparse signals, where a part of the support, and an approximate estimate of the sparse signal are known. This method is useful, e.g., in magnetic resonance image (MRI) sequence, natural video sequences, etc, where it is required to recursively reconstruct a sequence of mutually correlated sparse signals or images. Here we use the last signal as an a priori estimate of the current signal. The priori information is often inaccurate, and we adopt MAP estimation framework to deal with this issue. Simulation studies are performed, and the algorithm is applied to reconstruct MRI image sequences.
- Subject
- maximum a posteriori; Gaussian mixture model; partially known support; compressive sensing
- Identifier
- http://hdl.handle.net/1959.13/1342277
- Identifier
- uon:28930
- Identifier
- ISBN:9781479923410
- Rights
- © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
- Language
- eng
- Full Text
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