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- Publisher Website: 10.1007/s10851-005-2028-5
- Scopus: eid_2-s2.0-24944464196
- WOS: WOS:000231760200009
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Article: Super-resolution image restoration from blurred low-resolution images
Title | Super-resolution image restoration from blurred low-resolution images |
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Authors | |
Keywords | Image restoration Preconditioned conjugate gradient method High-resolution Regularization |
Issue Date | 2005 |
Citation | Journal of Mathematical Imaging and Vision, 2005, v. 23, n. 3, p. 367-378 How to Cite? |
Abstract | In this paper, we study the problem of reconstructing a high-resolution image from several blurred low-resolution image frames. The image frames consist of decimated, blurred and noisy versions of the high-resolution image. The high-resolution image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that with the periodic boundary condition, the high-resolution image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore the high-resolution image. Computer simulations are given to illustrate the effectiveness of the proposed method. © 2005 Springer Science + Business Media, Inc. |
Persistent Identifier | http://hdl.handle.net/10722/276778 |
ISSN | 2023 Impact Factor: 1.3 2023 SCImago Journal Rankings: 0.684 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Yau, Andy C. | - |
dc.date.accessioned | 2019-09-18T08:34:38Z | - |
dc.date.available | 2019-09-18T08:34:38Z | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | Journal of Mathematical Imaging and Vision, 2005, v. 23, n. 3, p. 367-378 | - |
dc.identifier.issn | 0924-9907 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276778 | - |
dc.description.abstract | In this paper, we study the problem of reconstructing a high-resolution image from several blurred low-resolution image frames. The image frames consist of decimated, blurred and noisy versions of the high-resolution image. The high-resolution image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that with the periodic boundary condition, the high-resolution image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore the high-resolution image. Computer simulations are given to illustrate the effectiveness of the proposed method. © 2005 Springer Science + Business Media, Inc. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Mathematical Imaging and Vision | - |
dc.subject | Image restoration | - |
dc.subject | Preconditioned conjugate gradient method | - |
dc.subject | High-resolution | - |
dc.subject | Regularization | - |
dc.title | Super-resolution image restoration from blurred low-resolution images | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s10851-005-2028-5 | - |
dc.identifier.scopus | eid_2-s2.0-24944464196 | - |
dc.identifier.volume | 23 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 367 | - |
dc.identifier.epage | 378 | - |
dc.identifier.isi | WOS:000231760200009 | - |
dc.identifier.issnl | 0924-9907 | - |