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- Publisher Website: 10.1023/A:1011136812633
- Scopus: eid_2-s2.0-0035305125
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Article: A fast MAP algorithm for high-resolution image reconstruction with multisensors
Title | A fast MAP algorithm for high-resolution image reconstruction with multisensors |
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Authors | |
Keywords | Cosine transform MAP Structured matrices Image reconstruction Regularization |
Issue Date | 2001 |
Citation | Multidimensional Systems and Signal Processing, 2001, v. 12, n. 2, p. 143-164 How to Cite? |
Abstract | In many applications, it is required to reconstruct a high-resolution image from multiple, undersampled and shifted noisy images. Using the regularization techniques such as the classical Tikhonov regularization and maximum a posteriori (MAP) procedure, a high-resolution image reconstruction algorithm is developed. Because of the blurring process, the boundary values of the low-resolution image are not completely determined by the original image inside the scene. This paper addresses how to use (i) the Neumann boundary condition on the image, i.e., we assume that the scene immediately outside is a reflection of the original scene at the boundary, and (ii) the preconditioned conjugate gradient method with cosine transform preconditioners to solve linear systems arising from the high-resolution image reconstruction with multisensors. The usefulness of the algorithm is demonstrated through simulated examples. |
Persistent Identifier | http://hdl.handle.net/10722/276724 |
ISSN | 2023 Impact Factor: 1.7 2023 SCImago Journal Rankings: 0.499 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Yip, Andy M. | - |
dc.date.accessioned | 2019-09-18T08:34:27Z | - |
dc.date.available | 2019-09-18T08:34:27Z | - |
dc.date.issued | 2001 | - |
dc.identifier.citation | Multidimensional Systems and Signal Processing, 2001, v. 12, n. 2, p. 143-164 | - |
dc.identifier.issn | 0923-6082 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276724 | - |
dc.description.abstract | In many applications, it is required to reconstruct a high-resolution image from multiple, undersampled and shifted noisy images. Using the regularization techniques such as the classical Tikhonov regularization and maximum a posteriori (MAP) procedure, a high-resolution image reconstruction algorithm is developed. Because of the blurring process, the boundary values of the low-resolution image are not completely determined by the original image inside the scene. This paper addresses how to use (i) the Neumann boundary condition on the image, i.e., we assume that the scene immediately outside is a reflection of the original scene at the boundary, and (ii) the preconditioned conjugate gradient method with cosine transform preconditioners to solve linear systems arising from the high-resolution image reconstruction with multisensors. The usefulness of the algorithm is demonstrated through simulated examples. | - |
dc.language | eng | - |
dc.relation.ispartof | Multidimensional Systems and Signal Processing | - |
dc.subject | Cosine transform | - |
dc.subject | MAP | - |
dc.subject | Structured matrices | - |
dc.subject | Image reconstruction | - |
dc.subject | Regularization | - |
dc.title | A fast MAP algorithm for high-resolution image reconstruction with multisensors | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1023/A:1011136812633 | - |
dc.identifier.scopus | eid_2-s2.0-0035305125 | - |
dc.identifier.volume | 12 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 143 | - |
dc.identifier.epage | 164 | - |
dc.identifier.isi | WOS:000168871400002 | - |
dc.identifier.issnl | 0923-6082 | - |