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Article: Structured total least squares for color image restoration
| Title | Structured total least squares for color image restoration |
|---|---|
| Authors | |
| Keywords | Preconditioners Structured total least squares Toeplitz-like matrices Color image Image restoration |
| Issue Date | 2006 |
| Citation | SIAM Journal on Scientific Computing, 2006, v. 28, n. 3, p. 1100-1119 How to Cite? |
| Abstract | The problem of 3 × 3 color mixing image restoration is considered. The blurring matrices, as well as the observed image, are contaminated by noise; therefore the total least squares (TLS) method is employed to restore the original image. Since the blurring matrices are also structured, we apply the structured total least squares (STLS) method [J. B. Rosen, H. Park, and J. Glick, SIAM J. Matrix Anal. Appl., 17 (1996), pp. 110-126]. The blurring matrices are generally ill conditioned; thus Tikhonov's regularization is used to stabilize the solution. Since Neumann boundary conditions are used in the restoration process, the discrete cosine transform (DCT) based preconditioner is effective for the linear systems encountered in our STLS algorithm. © 2006 Society for Industrial and Applied Mathematics. |
| Persistent Identifier | http://hdl.handle.net/10722/276810 |
| ISSN | 2021 Impact Factor: 2.968 2020 SCImago Journal Rankings: 1.674 |
| ISI Accession Number ID |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Fu, Haoying | - |
| dc.contributor.author | Ng, Michael K. | - |
| dc.contributor.author | Barlow, Jesse L. | - |
| dc.date.accessioned | 2019-09-18T08:34:43Z | - |
| dc.date.available | 2019-09-18T08:34:43Z | - |
| dc.date.issued | 2006 | - |
| dc.identifier.citation | SIAM Journal on Scientific Computing, 2006, v. 28, n. 3, p. 1100-1119 | - |
| dc.identifier.issn | 1064-8275 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/276810 | - |
| dc.description.abstract | The problem of 3 × 3 color mixing image restoration is considered. The blurring matrices, as well as the observed image, are contaminated by noise; therefore the total least squares (TLS) method is employed to restore the original image. Since the blurring matrices are also structured, we apply the structured total least squares (STLS) method [J. B. Rosen, H. Park, and J. Glick, SIAM J. Matrix Anal. Appl., 17 (1996), pp. 110-126]. The blurring matrices are generally ill conditioned; thus Tikhonov's regularization is used to stabilize the solution. Since Neumann boundary conditions are used in the restoration process, the discrete cosine transform (DCT) based preconditioner is effective for the linear systems encountered in our STLS algorithm. © 2006 Society for Industrial and Applied Mathematics. | - |
| dc.language | eng | - |
| dc.relation.ispartof | SIAM Journal on Scientific Computing | - |
| dc.subject | Preconditioners | - |
| dc.subject | Structured total least squares | - |
| dc.subject | Toeplitz-like matrices | - |
| dc.subject | Color image | - |
| dc.subject | Image restoration | - |
| dc.title | Structured total least squares for color image restoration | - |
| dc.type | Article | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1137/040605436 | - |
| dc.identifier.scopus | eid_2-s2.0-34249109703 | - |
| dc.identifier.volume | 28 | - |
| dc.identifier.issue | 3 | - |
| dc.identifier.spage | 1100 | - |
| dc.identifier.epage | 1119 | - |
| dc.identifier.isi | WOS:000239701100014 | - |
| dc.identifier.issnl | 1064-8275 | - |