A computational algorithm for minimizing total variation in image restoration
A reliable and efficient computational algorithm for restoring blurred and noisy images is proposed. The restoration process is based on the minimal total variation principle introduced by Rudin et al. For discrete images, the proposed algorithm minimizes a piecewise linear l/sub 1/ function (a meas...
Uloženo v:
| Vydáno v: | IEEE transactions on image processing Ročník 5; číslo 6; s. 987 - 995 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
IEEE
01.06.1996
|
| Témata: | |
| ISSN: | 1057-7149 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | A reliable and efficient computational algorithm for restoring blurred and noisy images is proposed. The restoration process is based on the minimal total variation principle introduced by Rudin et al. For discrete images, the proposed algorithm minimizes a piecewise linear l/sub 1/ function (a measure of total variation) subject to a single 2-norm inequality constraint (a measure of data fit). The algorithm starts by finding a feasible point for the inequality constraint using a (partial) conjugate gradient method. This corresponds to a deblurring process. Noise and other artifacts are removed by a subsequent total variation minimization process. The use of the linear l/sub 1/ objective function for the total variation measurement leads to a simpler computational algorithm. Both the steepest descent and an affine scaling Newton method are considered to solve this constrained piecewise linear l/sub 1/ minimization problem. The resulting algorithm, when viewed as an image restoration and enhancement process, has the feature that it can be used in an adaptive/interactive manner in situations when knowledge of the noise variance is either unavailable or unreliable. Numerical examples are presented to demonstrate the effectiveness of the proposed iterative image restoration and enhancement process. |
|---|---|
| AbstractList | A reliable and efficient computational algorithm for restoring blurred and noisy images is proposed. The restoration process is based on the minimal total variation principle introduced by Rudin et al. For discrete images, the proposed algorithm minimizes a piecewise linear l (1) function (a measure of total variation) subject to a single 2-norm inequality constraint (a measure of data fit). The algorithm starts by finding a feasible point for the inequality constraint using a (partial) conjugate gradient method. This corresponds to a deblurring process. Noise and other artifacts are removed by a subsequent total variation minimization process. The use of the linear l(1) objective function for the total variation measurement leads to a simpler computational algorithm. Both the steepest descent and an affine scaling Newton method are considered to solve this constrained piecewise linear l(1) minimization problem. The resulting algorithm, when viewed as an image restoration and enhancement process, has the feature that it can be used in an adaptive/interactive manner in situations when knowledge of the noise variance is either unavailable or unreliable. Numerical examples are presented to demonstrate the effectiveness of the proposed iterative image restoration and enhancement process A reliable and efficient computational algorithm for restoring blurred and noisy images is proposed. The restoration process is based on the minimal total variation principle introduced by Rudin et al. For discrete images, the proposed algorithm minimizes a piecewise linear l (1) function (a measure of total variation) subject to a single 2-norm inequality constraint (a measure of data fit). The algorithm starts by finding a feasible point for the inequality constraint using a (partial) conjugate gradient method. This corresponds to a deblurring process. Noise and other artifacts are removed by a subsequent total variation minimization process. The use of the linear l(1) objective function for the total variation measurement leads to a simpler computational algorithm. Both the steepest descent and an affine scaling Newton method are considered to solve this constrained piecewise linear l(1) minimization problem. The resulting algorithm, when viewed as an image restoration and enhancement process, has the feature that it can be used in an adaptive/interactive manner in situations when knowledge of the noise variance is either unavailable or unreliable. Numerical examples are presented to demonstrate the effectiveness of the proposed iterative image restoration and enhancement process. A reliable and efficient computational algorithm for restoring blurred and noisy images is proposed. The restoration process is based on the minimal total variation principle introduced by Rudin et al. For discrete images, the proposed algorithm minimizes a piecewise linear l sub(1) function (a measure of total variation) subject to a single 2-norm inequality constraint (a measure of data fit). The algorithm starts by finding a feasible point for the inequality constraint using a (partial) conjugate gradient method. This corresponds to a deblurring process. Noise and other artifacts are removed by a subsequent total variation minimization process. The use of the linear l sub(1) objective function for the total variation measurement leads to a simplier computational algorithm. Both the steepest descent and an affine scaling Newton method are considered to solve this constrained piecewise linear l sub(1) minimization problem. The resulting algorithm, when viewed as an image restoration and enhancement process, has the feature that it can be used in an adaptive/interactive manner in situations when knowledge of the noise variance is either unavailable or unreliable. Numerical examples are presented to demonstrate the effectiveness of the proposed iterative image restoration and enhancement process. A reliable and efficient computational algorithm for restoring blurred and noisy images is proposed. The restoration process is based on the minimal total variation principle introduced by Rudin et al. For discrete images, the proposed algorithm minimizes a piecewise linear l/sub 1/ function (a measure of total variation) subject to a single 2-norm inequality constraint (a measure of data fit). The algorithm starts by finding a feasible point for the inequality constraint using a (partial) conjugate gradient method. This corresponds to a deblurring process. Noise and other artifacts are removed by a subsequent total variation minimization process. The use of the linear l/sub 1/ objective function for the total variation measurement leads to a simpler computational algorithm. Both the steepest descent and an affine scaling Newton method are considered to solve this constrained piecewise linear l/sub 1/ minimization problem. The resulting algorithm, when viewed as an image restoration and enhancement process, has the feature that it can be used in an adaptive/interactive manner in situations when knowledge of the noise variance is either unavailable or unreliable. Numerical examples are presented to demonstrate the effectiveness of the proposed iterative image restoration and enhancement process. A reliable and efficient computational algorithm for restoring blurred and noisy images is proposed. The restoration process is based on the minimal total variation principle introduced by Rudin et al. For discrete images, the proposed algorithm minimizes a piecewise linear l (1) function (a measure of total variation) subject to a single 2-norm inequality constraint (a measure of data fit). The algorithm starts by finding a feasible point for the inequality constraint using a (partial) conjugate gradient method. This corresponds to a deblurring process. Noise and other artifacts are removed by a subsequent total variation minimization process. The use of the linear l(1) objective function for the total variation measurement leads to a simpler computational algorithm. Both the steepest descent and an affine scaling Newton method are considered to solve this constrained piecewise linear l(1) minimization problem. The resulting algorithm, when viewed as an image restoration and enhancement process, has the feature that it can be used in an adaptive/interactive manner in situations when knowledge of the noise variance is either unavailable or unreliable. Numerical examples are presented to demonstrate the effectiveness of the proposed iterative image restoration and enhancement process.A reliable and efficient computational algorithm for restoring blurred and noisy images is proposed. The restoration process is based on the minimal total variation principle introduced by Rudin et al. For discrete images, the proposed algorithm minimizes a piecewise linear l (1) function (a measure of total variation) subject to a single 2-norm inequality constraint (a measure of data fit). The algorithm starts by finding a feasible point for the inequality constraint using a (partial) conjugate gradient method. This corresponds to a deblurring process. Noise and other artifacts are removed by a subsequent total variation minimization process. The use of the linear l(1) objective function for the total variation measurement leads to a simpler computational algorithm. Both the steepest descent and an affine scaling Newton method are considered to solve this constrained piecewise linear l(1) minimization problem. The resulting algorithm, when viewed as an image restoration and enhancement process, has the feature that it can be used in an adaptive/interactive manner in situations when knowledge of the noise variance is either unavailable or unreliable. Numerical examples are presented to demonstrate the effectiveness of the proposed iterative image restoration and enhancement process. |
| Author | Yuying Li Santosa, F. |
| Author_xml | – sequence: 1 surname: Yuying Li fullname: Yuying Li organization: Dept. of Comput. Sci., Cornell Univ., Ithaca, NY, USA – sequence: 2 givenname: F. surname: Santosa fullname: Santosa, F. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/18285186$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkU1PwzAMhnMYYh9w4MoB9QTiUBY3aZoep4kvaYgLnKO0dUdQ24y0Q4JfT7aOHRACyZJl-_Fr6fWYDBrbICEnQK8AaDqV7CqmLAU-ICOgcRImwNMhGbftK6XAYxCHZAgykjFIMSIPsyC39Wrd6c7YRleBrpbWme6lDkrrgto0pjafplkGne38-F07s0UD46PWSwwctp112-YROSh11eLxLk_I88310_wuXDze3s9nizBngnUh5EmaFnmEKZU8B5lpZCUKFqHUEeOCFxkVWVpmJcS-TIUP5LSQSQkF0oJNyEWvu3L2be3vq9q0OVaVbtCuW5UwHiUR58yT53-SkYyAMx7_DwrY2LpRPNuB66zGQq2ct8F9qG9PPTDtgdzZtnVYqtz09nZOm0oBVZtPKclU_ym_cfljYy_6C3vaswYR99xu-AWkH5xV |
| CODEN | IIPRE4 |
| CitedBy_id | crossref_primary_10_1023_B_JMIV_0000011325_36760_1e crossref_primary_10_1002_sca_21127 crossref_primary_10_1137_090774823 crossref_primary_10_1155_IJBI_2006_83847 crossref_primary_10_1007_s11760_024_03083_7 crossref_primary_10_1007_s11075_006_9020_z crossref_primary_10_1155_2017_3012910 crossref_primary_10_1007_s10851_009_0137_2 crossref_primary_10_1023_A_1011243521400 crossref_primary_10_1007_s10851_005_6467_9 crossref_primary_10_1155_2013_217021 crossref_primary_10_1007_s11075_012_9623_5 crossref_primary_10_1109_TGRS_2020_3007945 crossref_primary_10_1186_s12938_017_0318_y crossref_primary_10_1007_s00285_011_0402_z crossref_primary_10_1155_2014_356906 crossref_primary_10_1049_ip_vis_20020421 crossref_primary_10_1109_TIP_2007_908079 crossref_primary_10_1109_TIM_2002_808026 crossref_primary_10_1109_TIP_2008_2008420 crossref_primary_10_1016_j_cviu_2017_08_007 crossref_primary_10_1155_2007_74585 crossref_primary_10_1109_TMI_2014_2324900 crossref_primary_10_1080_17415977_2018_1500569 crossref_primary_10_1088_0266_5611_27_4_045009 crossref_primary_10_1002_jcc_20796 crossref_primary_10_1137_080741410 crossref_primary_10_1137_S1064827596304010 crossref_primary_10_1016_j_jcp_2010_06_036 crossref_primary_10_3724_SP_J_1004_2012_00444 crossref_primary_10_1007_s00371_013_0857_6 crossref_primary_10_1016_j_patrec_2008_12_009 crossref_primary_10_1190_1_3506039 crossref_primary_10_1016_j_dsp_2024_104837 crossref_primary_10_1109_TMI_2003_819294 crossref_primary_10_1109_TMI_2007_911492 crossref_primary_10_1002_acm2_12411 crossref_primary_10_1016_S0531_5131_03_00485_0 crossref_primary_10_1109_78_950776 crossref_primary_10_1137_S0036139996313356 crossref_primary_10_1007_s10915_007_9145_9 crossref_primary_10_1109_JSTARS_2017_2707532 crossref_primary_10_1080_09500340_2015_1011246 crossref_primary_10_1137_S003614450037906X crossref_primary_10_1109_JSTARS_2022_3185657 crossref_primary_10_1016_j_amc_2021_126224 crossref_primary_10_1088_1361_6420_abb299 crossref_primary_10_1016_j_ijleo_2015_08_119 crossref_primary_10_1109_TIP_2002_804527 crossref_primary_10_1109_LSP_2007_906221 crossref_primary_10_1371_journal_pone_0202464 crossref_primary_10_1080_01431161_2016_1274444 crossref_primary_10_1023_A_1008344608808 crossref_primary_10_1137_070706318 crossref_primary_10_1007_s10915_013_9750_8 crossref_primary_10_1007_s40305_015_0078_y crossref_primary_10_1016_j_amc_2009_07_026 crossref_primary_10_1016_j_camwa_2024_08_014 crossref_primary_10_1088_1361_6501_aaaea4 crossref_primary_10_1109_TGRS_2019_2891354 crossref_primary_10_1016_j_eswa_2025_126948 crossref_primary_10_1371_journal_pone_0253214 crossref_primary_10_1016_j_nima_2014_09_041 crossref_primary_10_1007_s00285_008_0226_7 crossref_primary_10_1007_s11538_010_9511_x crossref_primary_10_1016_j_cam_2008_07_054 crossref_primary_10_1016_j_jcp_2015_10_036 crossref_primary_10_1109_83_679423 crossref_primary_10_1016_j_aeue_2017_06_023 crossref_primary_10_1016_j_amc_2013_05_070 crossref_primary_10_1137_070696143 crossref_primary_10_1177_16878132221136942 crossref_primary_10_1109_TIP_2004_834669 crossref_primary_10_1088_1361_6501_aac8b6 crossref_primary_10_1007_s12190_011_0528_6 crossref_primary_10_1007_s10851_007_0016_7 crossref_primary_10_1088_0266_5611_20_1_007 crossref_primary_10_1364_AO_405663 crossref_primary_10_1137_070703533 crossref_primary_10_1016_j_neunet_2025_107670 crossref_primary_10_1109_TCI_2017_2706062 crossref_primary_10_1109_TGRS_2019_2947253 crossref_primary_10_1371_journal_pone_0109345 crossref_primary_10_1155_ASP_2006_61859 crossref_primary_10_4028_www_scientific_net_AMM_321_324_1107 crossref_primary_10_1007_s00791_007_0060_2 crossref_primary_10_1137_050644999 crossref_primary_10_1088_0031_9155_55_13_022 crossref_primary_10_1007_s00791_004_0150_3 crossref_primary_10_1364_PRJ_447862 crossref_primary_10_1137_15100401X crossref_primary_10_1186_s13660_017_1433_9 crossref_primary_10_1007_s00371_014_1007_5 crossref_primary_10_1093_mnras_stv233 crossref_primary_10_1007_s00371_013_0873_6 crossref_primary_10_1137_15M1034076 |
| Cites_doi | 10.1007/BF01580899 10.1109/78.80914 10.1007/BF02579150 10.1137/0727053 10.1137/0109044 10.1016/0167-2789(92)90242-F 10.1007/BF02592024 10.1109/TC.1973.5009169 10.1109/78.277854 10.1137/0710069 10.1088/0266-5611/10/2/008 10.1109/83.236536 10.1051/m2an:1999102 10.1109/29.9032 10.1007/978-1-4615-3980-3 |
| ContentType | Journal Article |
| DBID | AAYXX CITATION NPM 7SC 8FD JQ2 L7M L~C L~D 7X8 |
| DOI | 10.1109/83.503914 |
| DatabaseName | CrossRef PubMed Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitleList | Computer and Information Systems Abstracts PubMed Computer and Information Systems Abstracts MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Mathematics Applied Sciences Computer Science |
| EndPage | 995 |
| ExternalDocumentID | 18285186 10_1109_83_503914 503914 |
| Genre | Journal Article |
| GroupedDBID | --- -~X .DC 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABFSI ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD F5P HZ~ H~9 ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P RIA RIE RNS TAE TN5 VH1 AAYXX CITATION AAYOK NPM RIG 7SC 8FD JQ2 L7M L~C L~D 7X8 |
| ID | FETCH-LOGICAL-c363t-1c799dc2e9084c18bae3fe632e8a23464db06b9fbf1534696696e40d87f1de0d3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 138 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=10_1109_83_503914&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1057-7149 |
| IngestDate | Sun Nov 09 09:02:59 EST 2025 Sun Sep 28 09:44:26 EDT 2025 Sun Nov 09 11:59:00 EST 2025 Thu Apr 03 07:06:50 EDT 2025 Sat Nov 29 06:27:22 EST 2025 Tue Nov 18 22:35:44 EST 2025 Tue Aug 26 20:56:29 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c363t-1c799dc2e9084c18bae3fe632e8a23464db06b9fbf1534696696e40d87f1de0d3 |
| Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
| PMID | 18285186 |
| PQID | 26103913 |
| PQPubID | 23500 |
| PageCount | 9 |
| ParticipantIDs | proquest_miscellaneous_734272443 ieee_primary_503914 pubmed_primary_18285186 crossref_citationtrail_10_1109_83_503914 proquest_miscellaneous_28214345 crossref_primary_10_1109_83_503914 proquest_miscellaneous_26103913 |
| PublicationCentury | 1900 |
| PublicationDate | 1996-06-01 |
| PublicationDateYYYYMMDD | 1996-06-01 |
| PublicationDate_xml | – month: 06 year: 1996 text: 1996-06-01 day: 01 |
| PublicationDecade | 1990 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | IEEE transactions on image processing |
| PublicationTitleAbbrev | TIP |
| PublicationTitleAlternate | IEEE Trans Image Process |
| PublicationYear | 1996 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| References | ref13 ref12 ref10 ref1 (ref15) 1992 ref17 ref19 bartels (ref18) 1981 katsaggelos (ref7) 1985 rudin (ref2) 0 ref23 ref25 ref20 dobson (ref11) 1994 vogel (ref14) 0 ref8 ref9 ref4 meketon (ref21) 1988 ref3 ref6 ref5 zhang (ref22) 1991 golub (ref16) 1989 sun (ref24) 1995 |
| References_xml | – year: 1994 ident: ref11 publication-title: Recovery of blocky images from noisy and blurred data – year: 1981 ident: ref18 publication-title: An approach to nonlinear $l_1$ data fitting – ident: ref23 doi: 10.1007/BF01580899 – ident: ref8 doi: 10.1109/78.80914 – ident: ref19 doi: 10.1007/BF02579150 – year: 1988 ident: ref21 publication-title: Least absolute value regression – ident: ref1 doi: 10.1137/0727053 – ident: ref12 doi: 10.1137/0109044 – ident: ref3 doi: 10.1016/0167-2789(92)90242-F – ident: ref20 doi: 10.1007/BF02592024 – ident: ref5 doi: 10.1109/TC.1973.5009169 – ident: ref10 doi: 10.1109/78.277854 – start-page: 700 year: 1985 ident: ref7 article-title: a general formulation of constrained iterative image restoration publication-title: Proc ICASSP-85 – year: 1989 ident: ref16 publication-title: Matrix Computations – ident: ref17 doi: 10.1137/0710069 – year: 1992 ident: ref15 publication-title: MATLAB Reference Guide – ident: ref25 doi: 10.1088/0266-5611/10/2/008 – ident: ref9 doi: 10.1109/83.236536 – ident: ref13 doi: 10.1051/m2an:1999102 – year: 1991 ident: ref22 publication-title: A primal-dual interior point approach for computing the $l_1$ and $l_\infty$ solutions of overdetermined linear systems – year: 1995 ident: ref24 publication-title: Dealing with dense rows in the solution of sparse linear least squares problems – year: 0 ident: ref14 publication-title: Iterative methods for total variation denoising – ident: ref6 doi: 10.1109/29.9032 – year: 0 ident: ref2 article-title: total variation based restoration of noisy blurred images publication-title: SIAM J Num Anal – ident: ref4 doi: 10.1007/978-1-4615-3980-3 |
| SSID | ssj0014516 |
| Score | 1.9199294 |
| Snippet | A reliable and efficient computational algorithm for restoring blurred and noisy images is proposed. The restoration process is based on the minimal total... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 987 |
| SubjectTerms | Computer science Gradient methods Image restoration Iterative algorithms Least squares methods Mathematics Military computing Minimization methods Newton method Piecewise linear techniques |
| Title | A computational algorithm for minimizing total variation in image restoration |
| URI | https://ieeexplore.ieee.org/document/503914 https://www.ncbi.nlm.nih.gov/pubmed/18285186 https://www.proquest.com/docview/26103913 https://www.proquest.com/docview/28214345 https://www.proquest.com/docview/734272443 |
| Volume | 5 |
| WOSCitedRecordID | wos10_1109_83_503914&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) issn: 1057-7149 databaseCode: RIE dateStart: 19920101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://ieeexplore.ieee.org/ omitProxy: false ssIdentifier: ssj0014516 providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB5UPOjBx_paH2sQD16qbZPmcRRRvKx4UNhbSZtUF9yt7HY9-OudpO2qoIKQQztMSujk8U0m-Qbg1FG0Oya7QFMjA_S_cB5MCh1oJlgidZZkqvDJJsTdnRwM1H3Ds-3vwlhr_eEze-4efSzflPnMbZVdJI7OnC3CohCivqo1Dxi4fLM-sJmIQCDqb0iEolBdSHpeV_y29PhcKr_DSr-83Kz_q2EbsNagSHJZm30TFuy4A-sNoiTNeJ2iqE3a0Mo6sPqFgRDf-nPa1ukW9C9J7is0O4REvzyVk2H1PCKIbYmjIRkN37EiqUoE7eQNHW2vSoZYRjg1kYnPVOOF2_B4c_1wdRs0-RaCnHJaBVEulDJ5bBVaMI9kpi0tLKexlTqmjDOThRxtlxU4TaJZORbLQiNFERkbGroDS-NybPeAcGqZEhlaG5d_LZU0BddCFqHhcSaTsAtnrSnSvCEjdzkxXlLvlIQqlTSt_2oXTuaqrzUDx09KHWeVuUIrPW7Nm-KocaEQPbblbJqi3-g06B8aMkYoyZIukF80BGWxQHSEH9mtu85n-xwvYCT5_o_NOoCV-uy328s5hKVqMrNHsJy_VcPppIe9eyB7vnd_AE3k9xo |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NT9swFH_aOqSNAx8dg7IBFuLAJZDEjj-OaFrFBK04FIlb5MQOVKINalMO--v37CQFpDJpkg_J03Nk5fnj9_zs3wM4cRTtjsku0NTIAP0vnAeTQgeaCZZInSWZKnyyCTEcyrs7ddPwbPu7MNZaf_jMnrlHH8s3Zb5wW2XniaMzZx_hU8JYHNWXtZYhA5dx1oc2ExEIxP0NjVAUqnNJz-qqbxYfn03lfWDpF5j-5n81bQs2GhxJLmrDb8MHO-3CZoMpSTNi5yhq0za0si6sv-IgxLfBkrh1_hUGFyT3FZo9QqIf78vZuHqYEES3xBGRTMZ_sCKpSoTt5Bldba9KxlgmODmRmc9V44U7cNv_Nfp5GTQZF4KccloFUS6UMnlsFdowj2SmLS0sp7GVOqaMM5OFHK2XFThRomE5FstCI0URGRsa-g0603Jq94BwapkSGdobAYCWSpqCayGL0PA4k0nYg9PWFGne0JG7rBiPqXdLQpVKmtZ_tQfHS9WnmoNjlVLXWWWp0EqPWvOmOG5cMERPbbmYp-g5Og36Dw0ZI5hkSQ_IOxqCslggPsKP7NZd56V9jhkwknx_ZbOO4PPlaHCdXv8eXn2HL_VJcLez8wM61WxhD2Atf67G89mh7-N_AXIk-Xk |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+computational+algorithm+for+minimizing+total+variation+in+image+restoration&rft.jtitle=IEEE+transactions+on+image+processing&rft.au=Li%2C+Y&rft.au=Santosa%2C+F&rft.date=1996-06-01&rft.issn=1057-7149&rft.volume=5&rft.issue=6&rft.spage=987&rft_id=info:doi/10.1109%2F83.503914&rft_id=info%3Apmid%2F18285186&rft.externalDocID=18285186 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1057-7149&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1057-7149&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1057-7149&client=summon |