Compressive Image Reconstruction in Reduced Union of Subspaces
We present a new compressed sensing framework for reconstruction of incomplete and possibly noisy images and their higher dimensional variants, e.g. animations and light‐fields. The algorithm relies on a learning‐based basis representation. We train an ensemble of intrinsically two‐dimensional (2D)...
Saved in:
| Published in | Computer graphics forum Vol. 34; no. 2; pp. 33 - 44 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.05.2015
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-7055 1467-8659 1467-8659 |
| DOI | 10.1111/cgf.12539 |
Cover
| Abstract | We present a new compressed sensing framework for reconstruction of incomplete and possibly noisy images and their higher dimensional variants, e.g. animations and light‐fields. The algorithm relies on a learning‐based basis representation. We train an ensemble of intrinsically two‐dimensional (2D) dictionaries that operate locally on a set of 2D patches extracted from the input data. We show that one can convert the problem of 2D sparse signal recovery to an equivalent 1D form, enabling us to utilize a large family of sparse solvers. The proposed framework represents the input signals in a reduced union of subspaces model, while allowing sparsity in each subspace. Such a model leads to a much more sparse representation than widely used methods such as K‐SVD. To evaluate our method, we apply it to three different scenarios where the signal dimensionality varies from 2D (images) to 3D (animations) and 4D (light‐fields). We show that our method outperforms state‐of‐the‐art algorithms in computer graphics and image processing literature. |
|---|---|
| AbstractList | We present a new compressed sensing framework for reconstruction of incomplete and possibly noisy images and their higher dimensional variants, e.g. animations and light‐fields. The algorithm relies on a learning‐based basis representation. We train an ensemble of intrinsically two‐dimensional (2D) dictionaries that operate locally on a set of 2D patches extracted from the input data. We show that one can convert the problem of 2D sparse signal recovery to an equivalent 1D form, enabling us to utilize a large family of sparse solvers. The proposed framework represents the input signals in a reduced union of subspaces model, while allowing sparsity in each subspace. Such a model leads to a much more sparse representation than widely used methods such as K‐SVD. To evaluate our method, we apply it to three different scenarios where the signal dimensionality varies from 2D (images) to 3D (animations) and 4D (light‐fields). We show that our method outperforms state‐of‐the‐art algorithms in computer graphics and image processing literature. |
| Author | Miandji, Ehsan Kronander, Joel Unger, Jonas |
| Author_xml | – sequence: 1 givenname: Ehsan surname: Miandji fullname: Miandji, Ehsan email: ehsan.miandji@liu.se organization: Linköping University, Sweden – sequence: 2 givenname: Joel surname: Kronander fullname: Kronander, Joel organization: Linköping University, Sweden – sequence: 3 givenname: Jonas surname: Unger fullname: Unger, Jonas organization: Linköping University, Sweden |
| BackLink | https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119639$$DView record from Swedish Publication Index |
| BookMark | eNp9kE1vEzEQhi3USqSlB_7BSlwAadtx1p8XpDbQtFIEUqGUm-X1eiOXzXqx14T8e5ym6iEqzMG2Rs878jNH6KD3vUXoNYZTnOvMLNtTPKWVfIEmmDBeCkblAZoAzm8OlL5ERzHeAwDhjE7Qh5lfDcHG6H7b4nqll7a4scb3cQzJjM73hetzp0nGNsVtv234tvia6jhoY-MrdNjqLtqTx_sY3V5--ja7Khdf5tez80VpCMGy1GIKDQgjWkMJAdAtptYSJutGGmBCaD5luJFQ66aCpqq5lFAZ1tIG1wJkdYze7-amftCbte46NQS30mGjMKitucrm6sE8w-UOjms7pPqJ9Nqpj-77ufJhqTqXck6yB_7tjh-C_5VsHNXKRWO7TvfWp6gw5wLyQUhG3-yh9z6FPqsrzCRgQXNl6mxHmeBjDLZVxo16u80xaNc9--V3e4n_6T1OX7vObv4Nqtn8cm8hLo72z1NCh5-K8YpTdfd5ru7mi-nFgl-pH9VfZz2yKw |
| CitedBy_id | crossref_primary_10_1109_TCI_2021_3077131 crossref_primary_10_1016_j_cviu_2017_06_009 crossref_primary_10_1111_cgf_12819 crossref_primary_10_1109_TVCG_2017_2722414 crossref_primary_10_1177_1748301816673074 crossref_primary_10_1007_s00034_017_0602_x crossref_primary_10_1111_cgf_13835 crossref_primary_10_1016_j_image_2020_116087 crossref_primary_10_1145_3269980 crossref_primary_10_1109_TCI_2021_3053702 crossref_primary_10_1109_TII_2020_3025182 crossref_primary_10_1109_ACCESS_2021_3059887 crossref_primary_10_1109_TCI_2021_3132191 crossref_primary_10_1109_TVCG_2024_3355200 crossref_primary_10_1109_ACCESS_2022_3168362 |
| Cites_doi | 10.1214/009053604000000067 10.1109/TIP.2009.2034991 10.1109/JPROC.2009.2038076 10.1109/TIP.2006.881969 10.1145/1618452.1618486 10.1109/TIP.2011.2160072 10.1145/2010324.1964950 10.1109/TSP.2009.2016892 10.1109/TIP.2009.2022459 10.1109/LSP.2009.2017817 10.1145/2542355.2542385 10.1109/TVCG.2010.46 10.1109/TIP.2006.888330 10.1109/TIT.2006.871582 10.1145/1477926.1477929 10.1109/TSP.2011.2157912 10.1109/TIP.2011.2165289 10.1109/TIT.2011.2165821 10.1109/TIP.2011.2176743 10.1109/TSP.2012.2187642 10.1109/TPAMI.2012.39 10.1109/TSP.2006.881199 10.1109/TSP.2010.2044837 10.1145/258734.258775 10.1111/j.2517-6161.1996.tb02080.x 10.1145/2461912.2461914 10.1016/j.crma.2008.03.014 10.1109/TSP.2008.2007606 10.1145/1073204.1073224 10.1109/TIP.2007.911828 10.1111/j.1467-8659.2010.01731.x |
| ContentType | Journal Article |
| Copyright | 2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2015 The Eurographics Association and John Wiley & Sons Ltd. |
| Copyright_xml | – notice: 2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. – notice: 2015 The Eurographics Association and John Wiley & Sons Ltd. |
| DBID | BSCLL AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D F28 FR3 ABXSW ADTPV AOWAS D8T DG8 ZZAVC ADTOC UNPAY |
| DOI | 10.1111/cgf.12539 |
| DatabaseName | Istex CrossRef 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 ANTE: Abstracts in New Technology & Engineering Engineering Research Database SWEPUB Linköpings universitet full text SwePub SwePub Articles SWEPUB Freely available online SWEPUB Linköpings universitet SwePub Articles full text Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef 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 Engineering Research Database ANTE: Abstracts in New Technology & Engineering |
| DatabaseTitleList | CrossRef Computer and Information Systems Abstracts Technology Research Database |
| Database_xml | – sequence: 1 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1467-8659 |
| EndPage | 44 |
| ExternalDocumentID | oai:DiVA.org:liu-119639 oai_DiVA_org_liu_119639 3721827801 10_1111_cgf_12539 CGF12539 ark_67375_WNG_WGL2BL7H_X |
| Genre | article Feature |
| GroupedDBID | .3N .4S .DC .GA .Y3 05W 0R~ 10A 15B 1OB 1OC 29F 31~ 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5HH 5LA 5VS 66C 6J9 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 8VB 930 A03 AAESR AAEVG AAHQN AAMMB AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABDBF ABDPE ABEML ABPVW ACAHQ ACBWZ ACCZN ACFBH ACGFS ACPOU ACRPL ACSCC ACUHS ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADMLS ADNMO ADOZA ADXAS ADZMN AEFGJ AEGXH AEIGN AEIMD AEMOZ AENEX AEUYR AEYWJ AFBPY AFEBI AFFNX AFFPM AFGKR AFWVQ AFZJQ AGHNM AGQPQ AGXDD AGYGG AHBTC AHEFC AHQJS AIDQK AIDYY AIQQE AITYG AIURR AJXKR AKVCP ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ARCSS ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BSCLL BY8 CAG COF CS3 CWDTD D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM DU5 EAD EAP EBA EBO EBR EBS EBU EDO EJD EMK EST ESX F00 F01 F04 F5P FEDTE FZ0 G-S G.N GODZA H.T H.X HF~ HGLYW HVGLF HZI HZ~ I-F IHE IX1 J0M K1G K48 LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ O66 O9- OIG P2W P2X P4D PALCI PQQKQ Q.N Q11 QB0 QWB R.K RDJ RIWAO RJQFR ROL RX1 SAMSI SUPJJ TH9 TN5 TUS UB1 V8K W8V W99 WBKPD WIH WIK WOHZO WQJ WXSBR WYISQ WZISG XG1 ZL0 ZZTAW ~IA ~IF ~WT AAHHS ACCFJ ADZOD AEEZP AEQDE AEUQT AFPWT AIWBW AJBDE WRC AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D F28 FR3 ABXSW ADTPV AOWAS D8T DG8 ZZAVC ADTOC UNPAY |
| ID | FETCH-LOGICAL-c4419-a820d08c8fc54400af15ee469bd9c0688a7261d90bad30d3b79903c6f5d1b8093 |
| IEDL.DBID | DR2 |
| ISSN | 0167-7055 1467-8659 |
| IngestDate | Wed Aug 20 00:09:47 EDT 2025 Thu Aug 21 06:12:31 EDT 2025 Fri Sep 05 03:35:36 EDT 2025 Sun Sep 07 03:42:06 EDT 2025 Wed Oct 01 03:05:02 EDT 2025 Thu Apr 24 23:06:37 EDT 2025 Wed Jan 22 16:24:47 EST 2025 Sun Sep 21 06:17:57 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| License | other-oa |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c4419-a820d08c8fc54400af15ee469bd9c0688a7261d90bad30d3b79903c6f5d1b8093 |
| Notes | Supporting InformationSupporting Information ArticleID:CGF12539 istex:719C6E09B9806C60A2BBFDDD910E24F84661E5CB ark:/67375/WNG-WGL2BL7H-X SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119639 |
| PQID | 1690185555 |
| PQPubID | 30877 |
| PageCount | 12 |
| ParticipantIDs | unpaywall_primary_10_1111_cgf_12539 swepub_primary_oai_DiVA_org_liu_119639 proquest_miscellaneous_1778017744 proquest_journals_1690185555 crossref_citationtrail_10_1111_cgf_12539 crossref_primary_10_1111_cgf_12539 wiley_primary_10_1111_cgf_12539_CGF12539 istex_primary_ark_67375_WNG_WGL2BL7H_X |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | May 2015 |
| PublicationDateYYYYMMDD | 2015-05-01 |
| PublicationDate_xml | – month: 05 year: 2015 text: May 2015 |
| PublicationDecade | 2010 |
| PublicationPlace | Oxford |
| PublicationPlace_xml | – name: Oxford |
| PublicationTitle | Computer graphics forum |
| PublicationTitleAlternate | Computer Graphics Forum |
| PublicationYear | 2015 |
| Publisher | Blackwell Publishing Ltd |
| Publisher_xml | – name: Blackwell Publishing Ltd |
| References | Gleichman S., Eldar Y.: Blind compressed sensing. IEEE Transactions on Information Theory 57, 10 (2011), 6958-6975. 7 Marwah K., Wetzstein G., Bando Y., Raskar R.: Compressive light field photography using overcomplete dictionaries and optimized projections. ACM Transactions on Graphics (Proc. of SIGGRAPH) 32, 4 (2013), 46:1-46:12. 1, 3, 7, 10, 11 Sprechmann P., Ramirez I., Sapiro G., Eldar Y.: C-hilasso: A collaborative hierarchical sparse modeling framework. IEEE Transactions on Signal Processing 59, 9 (2011), 4183-4198. 6 Sen P., Darabi S.: Compressive rendering: A rendering application of compressed sensing. IEEE Transactions on Visualization and Computer Graphics 17, 4 (2011), 487-499. 1, 7, 8 Takeda H., Farsiu S., Milanfar P.: Kernel regression for image processing and reconstruction. IEEE Transactions on Image Processing 16, 2 (2007), 349-366. 7 Donoho D.: Compressed sensing. IEEE Transactions on Information Theory 52, 4 (April 2006), 1289-1306. 1 Overbeck R.S., Donner C., Ramamoorthi R.: Adaptive wavelet rendering. ACM Transactions on Graphics (Proc. of SIGGRAPH Asia) 28, 5 (2009), 140:1-140:12. 7 Baraniuk R., Cevher V., Wakin M.: Low-dimensional models for dimensionality reduction and signal recovery: A geometric perspective. Proceedings of the IEEE 98, 6 (2010), 959-971. 2, 6 Rivenson Y., Stern A.: Compressed imaging with a separable sensing operator. IEEE Signal Processing Letters 16, 6 (2009), 449-452. 4 Zhou M., Chen H., Paisley J., Ren L., Li L., Xing Z., Dunson D., Sapiro G., Carin L.: Nonparametric bayesian dictionary learning for analysis of noisy and incomplete images. IEEE Transactions on Image Processing 21, 1 (2012), 130-144. 10 Tibshirani R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 58 (1994), 267-288. 4 Duarte M., Baraniuk R.: Kronecker compressive sensing. IEEE Transactions on Image Processing 21, 2 (2012), 494-504. 4 Elad M., Aharon M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing 15, 12 (2006), 3736-3745. 6, 7 Aharon M., Elad M., Bruckstein A.: K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54, 11 (2006), 4311-4322. 3, 4, 6, 7, 8 Zelnik-Manor L., Rosenblum K., Eldar Y.: Dictionary optimization for block-sparse representations. IEEE Transactions on Signal Processing 60, 5 (2012), 2386-2395. 2, 6 Mairal J., Elad M., Sapiro G.: Sparse representation for color image restoration. IEEE Transactions on Image Processing 17, 1 (2008), 53-69. 8 Candès E. J.: The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique 346, 9-10 (2008), 589-592. 1 Eldar Y., Kuppinger P., Bolcskei H.: Block-sparse signals: Uncertainty relations and efficient recovery. IEEE Transactions on Signal Processing 58, 6 (2010), 3042-3054. 2, 6 Gurumoorthy K.S., Rajwade A., Banerjee A., Rangarajan A.: A method for compact image representation using sparse matrix and tensor projections onto exemplar orthonormal bases. IEEE Transactions on Image Processing 19, 2 (2010), 322-334. 3 Wang H., Wu Q., Shi L., Yu Y., Ahuja N.: Out-of-core tensor approximation of multi-dimensional matrices of visual data. ACM Transactions on Graphics (Proc. of SIGGRAPH) 24, 3 (2005), 527-535. 3 Mohimani H., Babaie-Zadeh M., Jutten C.: A fast approach for overcomplete sparse decomposition based on smoothed ℓ0 norm. IEEE Transactions on Signal Processing 57, 1 (2009), 289-301. 2, 7 Wright S., Nowak R., Figueiredo M.: Sparse reconstruction by separable approximation. IEEE Transactions on Signal Processing 57, 7 (2009), 2479-2493. 2, 11 Efron B., Hastie T., Johnstone I., Tibshirani R.: Least angle regression. Annals of Statistics 32, 2 (2004), 407-499. 2 Peers P., Mahajan D.K., Lamond B., Ghosh A., Matusik W., Ramamoorthi R., Debevec P.: Compressive light transport sensing. ACM Transactions on Graphics 28, 1 (2009), 3:1-3:18. 1, 11 Liu J., Musialski P., Wonka P., Ye J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 1 (2013), 208-220. 10 Yu G., Sapiro G., Mallat S.: Solving inverse problems with piecewise linear estimators: From gaussian mixture models to structured sparsity. IEEE Transactions on Image Processing 21, 5 (2012), 2481-2499. 10 Duarte-Carvajalino J., Sapiro G.: Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization. IEEE Transactions on Image Processing 18, 7 (2009), 1395-1408. 5 Lehtinen J., Aila T., Chen J., Laine S., Durand F.: Temporal light field reconstruction for rendering distribution effects. ACM Transactions on Graphics (Proc. of SIGGRAPH) 30, 4 (2011), 55:1-55:12. 7 Sen P., Darabi S.: Compressive estimation for signal integration in rendering. Computer Graphics Forum (Proc. of EGSR) 29, 4 (2010), 1355-1363. 1 2012; 60 2010; 98 2006; 52 2010; 58 2010; 19 2006; 54 2006; 15 2008; 17 2011; 30 1997 1995 2011; 57 2008; 346 2011; 59 2011; 17 2005; 24 2009; 28 2007; 16 2004; 32 2009; 57 2013; 32 2013; 35 2010; 29 1994; 58 2013 2012; 21 2009; 16 2009; 18 Veach E. (e_1_2_14_27_2) 1995 e_1_2_14_30_2 e_1_2_14_10_2 e_1_2_14_33_2 e_1_2_14_12_2 e_1_2_14_31_2 e_1_2_14_11_2 e_1_2_14_32_2 e_1_2_14_14_2 e_1_2_14_13_2 e_1_2_14_16_2 e_1_2_14_15_2 e_1_2_14_28_2 e_1_2_14_29_2 e_1_2_14_5_2 e_1_2_14_4_2 e_1_2_14_7_2 e_1_2_14_6_2 e_1_2_14_9_2 e_1_2_14_8_2 Tibshirani R. (e_1_2_14_26_2) 1994; 58 e_1_2_14_3_2 e_1_2_14_2_2 e_1_2_14_22_2 e_1_2_14_23_2 e_1_2_14_20_2 e_1_2_14_21_2 e_1_2_14_24_2 e_1_2_14_25_2 e_1_2_14_18_2 e_1_2_14_17_2 e_1_2_14_19_2 |
| References_xml | – reference: Mairal J., Elad M., Sapiro G.: Sparse representation for color image restoration. IEEE Transactions on Image Processing 17, 1 (2008), 53-69. 8 – reference: Efron B., Hastie T., Johnstone I., Tibshirani R.: Least angle regression. Annals of Statistics 32, 2 (2004), 407-499. 2 – reference: Rivenson Y., Stern A.: Compressed imaging with a separable sensing operator. IEEE Signal Processing Letters 16, 6 (2009), 449-452. 4 – reference: Sprechmann P., Ramirez I., Sapiro G., Eldar Y.: C-hilasso: A collaborative hierarchical sparse modeling framework. IEEE Transactions on Signal Processing 59, 9 (2011), 4183-4198. 6 – reference: Donoho D.: Compressed sensing. IEEE Transactions on Information Theory 52, 4 (April 2006), 1289-1306. 1 – reference: Wang H., Wu Q., Shi L., Yu Y., Ahuja N.: Out-of-core tensor approximation of multi-dimensional matrices of visual data. ACM Transactions on Graphics (Proc. of SIGGRAPH) 24, 3 (2005), 527-535. 3 – reference: Eldar Y., Kuppinger P., Bolcskei H.: Block-sparse signals: Uncertainty relations and efficient recovery. IEEE Transactions on Signal Processing 58, 6 (2010), 3042-3054. 2, 6 – reference: Marwah K., Wetzstein G., Bando Y., Raskar R.: Compressive light field photography using overcomplete dictionaries and optimized projections. ACM Transactions on Graphics (Proc. of SIGGRAPH) 32, 4 (2013), 46:1-46:12. 1, 3, 7, 10, 11 – reference: Sen P., Darabi S.: Compressive estimation for signal integration in rendering. Computer Graphics Forum (Proc. of EGSR) 29, 4 (2010), 1355-1363. 1 – reference: Liu J., Musialski P., Wonka P., Ye J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 1 (2013), 208-220. 10 – reference: Zhou M., Chen H., Paisley J., Ren L., Li L., Xing Z., Dunson D., Sapiro G., Carin L.: Nonparametric bayesian dictionary learning for analysis of noisy and incomplete images. IEEE Transactions on Image Processing 21, 1 (2012), 130-144. 10 – reference: Gurumoorthy K.S., Rajwade A., Banerjee A., Rangarajan A.: A method for compact image representation using sparse matrix and tensor projections onto exemplar orthonormal bases. IEEE Transactions on Image Processing 19, 2 (2010), 322-334. 3 – reference: Yu G., Sapiro G., Mallat S.: Solving inverse problems with piecewise linear estimators: From gaussian mixture models to structured sparsity. IEEE Transactions on Image Processing 21, 5 (2012), 2481-2499. 10 – reference: Overbeck R.S., Donner C., Ramamoorthi R.: Adaptive wavelet rendering. ACM Transactions on Graphics (Proc. of SIGGRAPH Asia) 28, 5 (2009), 140:1-140:12. 7 – reference: Candès E. J.: The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique 346, 9-10 (2008), 589-592. 1 – reference: Baraniuk R., Cevher V., Wakin M.: Low-dimensional models for dimensionality reduction and signal recovery: A geometric perspective. Proceedings of the IEEE 98, 6 (2010), 959-971. 2, 6 – reference: Wright S., Nowak R., Figueiredo M.: Sparse reconstruction by separable approximation. IEEE Transactions on Signal Processing 57, 7 (2009), 2479-2493. 2, 11 – reference: Gleichman S., Eldar Y.: Blind compressed sensing. IEEE Transactions on Information Theory 57, 10 (2011), 6958-6975. 7 – reference: Lehtinen J., Aila T., Chen J., Laine S., Durand F.: Temporal light field reconstruction for rendering distribution effects. ACM Transactions on Graphics (Proc. of SIGGRAPH) 30, 4 (2011), 55:1-55:12. 7 – reference: Sen P., Darabi S.: Compressive rendering: A rendering application of compressed sensing. IEEE Transactions on Visualization and Computer Graphics 17, 4 (2011), 487-499. 1, 7, 8 – reference: Duarte-Carvajalino J., Sapiro G.: Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization. IEEE Transactions on Image Processing 18, 7 (2009), 1395-1408. 5 – reference: Takeda H., Farsiu S., Milanfar P.: Kernel regression for image processing and reconstruction. IEEE Transactions on Image Processing 16, 2 (2007), 349-366. 7 – reference: Duarte M., Baraniuk R.: Kronecker compressive sensing. IEEE Transactions on Image Processing 21, 2 (2012), 494-504. 4 – reference: Zelnik-Manor L., Rosenblum K., Eldar Y.: Dictionary optimization for block-sparse representations. IEEE Transactions on Signal Processing 60, 5 (2012), 2386-2395. 2, 6 – reference: Mohimani H., Babaie-Zadeh M., Jutten C.: A fast approach for overcomplete sparse decomposition based on smoothed ℓ0 norm. IEEE Transactions on Signal Processing 57, 1 (2009), 289-301. 2, 7 – reference: Peers P., Mahajan D.K., Lamond B., Ghosh A., Matusik W., Ramamoorthi R., Debevec P.: Compressive light transport sensing. ACM Transactions on Graphics 28, 1 (2009), 3:1-3:18. 1, 11 – reference: Aharon M., Elad M., Bruckstein A.: K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54, 11 (2006), 4311-4322. 3, 4, 6, 7, 8 – reference: Tibshirani R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 58 (1994), 267-288. 4 – reference: Elad M., Aharon M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing 15, 12 (2006), 3736-3745. 6, 7 – volume: 60 start-page: 2386 issue: 5 year: 2012 end-page: 2395 article-title: Dictionary optimization for block‐sparse representations publication-title: IEEE Transactions on Signal Processing – volume: 57 start-page: 6958 issue: 10 year: 2011 end-page: 6975 article-title: Blind compressed sensing publication-title: IEEE Transactions on Information Theory – volume: 35 start-page: 208 issue: 1 year: 2013 end-page: 220 article-title: Tensor completion for estimating missing values in visual data publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – start-page: 65 year: 1997 end-page: 76 – volume: 58 start-page: 3042 issue: 6 year: 2010 end-page: 3054 article-title: Block‐sparse signals: Uncertainty relations and efficient recovery publication-title: IEEE Transactions on Signal Processing – volume: 57 start-page: 289 issue: 1 year: 2009 end-page: 301 article-title: A fast approach for overcomplete sparse decomposition based on smoothed ℓ norm publication-title: IEEE Transactions on Signal Processing – volume: 28 start-page: 140:1 issue: 5 year: 2009 end-page: 140:12 article-title: Adaptive wavelet rendering publication-title: ACM Transactions on Graphics (Proc. of SIGGRAPH Asia) – volume: 19 start-page: 322 issue: 2 year: 2010 end-page: 334 article-title: A method for compact image representation using sparse matrix and tensor projections onto exemplar orthonormal bases publication-title: IEEE Transactions on Image Processing – volume: 57 start-page: 2479 issue: 7 year: 2009 end-page: 2493 article-title: Sparse reconstruction by separable approximation publication-title: IEEE Transactions on Signal Processing – volume: 98 start-page: 959 issue: 6 year: 2010 end-page: 971 article-title: Low‐dimensional models for dimensionality reduction and signal recovery: A geometric perspective publication-title: Proceedings of the IEEE – volume: 28 start-page: 3:1 issue: 1 year: 2009 end-page: 3:18 article-title: Compressive light transport sensing publication-title: ACM Transactions on Graphics – volume: 17 start-page: 487 issue: 4 year: 2011 end-page: 499 article-title: Compressive rendering: A rendering application of compressed sensing publication-title: IEEE Transactions on Visualization and Computer Graphics – volume: 18 start-page: 1395 issue: 7 year: 2009 end-page: 1408 article-title: Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization publication-title: IEEE Transactions on Image Processing – volume: 21 start-page: 2481 issue: 5 year: 2012 end-page: 2499 article-title: Solving inverse problems with piecewise linear estimators: From gaussian mixture models to structured sparsity publication-title: IEEE Transactions on Image Processing – volume: 54 start-page: 4311 issue: 11 year: 2006 end-page: 4322 article-title: K‐SVD: An algorithm for designing overcomplete dictionaries for sparse representation publication-title: IEEE Transactions on Signal Processing – volume: 15 start-page: 3736 issue: 12 year: 2006 end-page: 3745 article-title: Image denoising via sparse and redundant representations over learned dictionaries publication-title: IEEE Transactions on Image Processing – start-page: 24:1 year: 2013 end-page: 24:4 – start-page: 145 year: 1995 end-page: 167 – volume: 16 start-page: 349 issue: 2 year: 2007 end-page: 366 article-title: Kernel regression for image processing and reconstruction publication-title: IEEE Transactions on Image Processing – volume: 21 start-page: 494 issue: 2 year: 2012 end-page: 504 article-title: Kronecker compressive sensing publication-title: IEEE Transactions on Image Processing – volume: 32 start-page: 407 issue: 2 year: 2004 end-page: 499 article-title: Least angle regression publication-title: Annals of Statistics – volume: 59 start-page: 4183 issue: 9 year: 2011 end-page: 4198 article-title: C‐hilasso: A collaborative hierarchical sparse modeling framework publication-title: IEEE Transactions on Signal Processing – volume: 21 start-page: 130 issue: 1 year: 2012 end-page: 144 article-title: Nonparametric bayesian dictionary learning for analysis of noisy and incomplete images publication-title: IEEE Transactions on Image Processing – volume: 32 start-page: 46:1 issue: 4 year: 2013 end-page: 46:12 article-title: Compressive light field photography using overcomplete dictionaries and optimized projections publication-title: ACM Transactions on Graphics (Proc. of SIGGRAPH) – volume: 30 start-page: 55:1 issue: 4 year: 2011 end-page: 55:12 article-title: Temporal light field reconstruction for rendering distribution effects publication-title: ACM Transactions on Graphics (Proc. of SIGGRAPH) – volume: 16 start-page: 449 issue: 6 year: 2009 end-page: 452 article-title: Compressed imaging with a separable sensing operator publication-title: IEEE Signal Processing Letters – volume: 17 start-page: 53 issue: 1 year: 2008 end-page: 69 article-title: Sparse representation for color image restoration publication-title: IEEE Transactions on Image Processing – volume: 52 start-page: 1289 issue: 4 year: 2006 end-page: 1306 article-title: Compressed sensing publication-title: IEEE Transactions on Information Theory – volume: 29 start-page: 1355 issue: 4 year: 2010 end-page: 1363 article-title: Compressive estimation for signal integration in rendering publication-title: Computer Graphics Forum (Proc. of EGSR) – volume: 58 start-page: 267 year: 1994 end-page: 288 article-title: Regression shrinkage and selection via the lasso publication-title: Journal of the Royal Statistical Society, Series B – volume: 346 start-page: 589 issue: 9‐10 year: 2008 end-page: 592 article-title: The restricted isometry property and its implications for compressed sensing publication-title: Comptes Rendus Mathematique – volume: 24 start-page: 527 issue: 3 year: 2005 end-page: 535 article-title: Out‐of‐core tensor approximation of multi‐dimensional matrices of visual data publication-title: ACM Transactions on Graphics (Proc. of SIGGRAPH) – ident: e_1_2_14_9_2 doi: 10.1214/009053604000000067 – ident: e_1_2_14_12_2 doi: 10.1109/TIP.2009.2034991 – ident: e_1_2_14_3_2 doi: 10.1109/JPROC.2009.2038076 – ident: e_1_2_14_8_2 doi: 10.1109/TIP.2006.881969 – ident: e_1_2_14_19_2 doi: 10.1145/1618452.1618486 – ident: e_1_2_14_32_2 doi: 10.1109/TIP.2011.2160072 – ident: e_1_2_14_13_2 doi: 10.1145/2010324.1964950 – ident: e_1_2_14_29_2 doi: 10.1109/TSP.2009.2016892 – ident: e_1_2_14_6_2 doi: 10.1109/TIP.2009.2022459 – ident: e_1_2_14_21_2 doi: 10.1109/LSP.2009.2017817 – ident: e_1_2_14_17_2 doi: 10.1145/2542355.2542385 – ident: e_1_2_14_23_2 doi: 10.1109/TVCG.2010.46 – ident: e_1_2_14_25_2 doi: 10.1109/TIP.2006.888330 – ident: e_1_2_14_7_2 doi: 10.1109/TIT.2006.871582 – ident: e_1_2_14_20_2 doi: 10.1145/1477926.1477929 – ident: e_1_2_14_24_2 doi: 10.1109/TSP.2011.2157912 – ident: e_1_2_14_5_2 doi: 10.1109/TIP.2011.2165289 – ident: e_1_2_14_11_2 doi: 10.1109/TIT.2011.2165821 – ident: e_1_2_14_31_2 doi: 10.1109/TIP.2011.2176743 – ident: e_1_2_14_33_2 doi: 10.1109/TSP.2012.2187642 – ident: e_1_2_14_14_2 doi: 10.1109/TPAMI.2012.39 – ident: e_1_2_14_2_2 doi: 10.1109/TSP.2006.881199 – ident: e_1_2_14_10_2 doi: 10.1109/TSP.2010.2044837 – ident: e_1_2_14_28_2 doi: 10.1145/258734.258775 – volume: 58 start-page: 267 year: 1994 ident: e_1_2_14_26_2 article-title: Regression shrinkage and selection via the lasso publication-title: Journal of the Royal Statistical Society, Series B doi: 10.1111/j.2517-6161.1996.tb02080.x – start-page: 145 volume-title: Focus on Computer Graphics year: 1995 ident: e_1_2_14_27_2 – ident: e_1_2_14_18_2 doi: 10.1145/2461912.2461914 – ident: e_1_2_14_4_2 doi: 10.1016/j.crma.2008.03.014 – ident: e_1_2_14_15_2 doi: 10.1109/TSP.2008.2007606 – ident: e_1_2_14_30_2 doi: 10.1145/1073204.1073224 – ident: e_1_2_14_16_2 doi: 10.1109/TIP.2007.911828 – ident: e_1_2_14_22_2 doi: 10.1111/j.1467-8659.2010.01731.x |
| SSID | ssj0004765 |
| Score | 2.252138 |
| Snippet | We present a new compressed sensing framework for reconstruction of incomplete and possibly noisy images and their higher dimensional variants, e.g. animations... |
| SourceID | unpaywall swepub proquest crossref wiley istex |
| SourceType | Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 33 |
| SubjectTerms | Algorithms Analysis and texture Animation Categories and Subject Descriptors (according to ACM CCS) compressed sensing Computer graphics I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism-Color I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism—Color, shading, shadowing, and texture Image processing systems Image reconstruction light field imaging Representations shading shadowing Studies Subspaces Two dimensional Unions |
| SummonAdditionalLinks | – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Zb9NAEB5B8gB94EYYCjKHqr448r3eSIBCIQmoRDwQGp5We1ZRjR21TTh-PbO-2opDYMmStR4fuzO78409-y3As4AIGaBj9qKMGw8tJPUyGUsvNIEhIgt5QO1E4fezdDqP3y2SxdmnC2y-Jskdg80y3-iXWPIc92EhiuGJHubL9VAtN9wLrOnQy9BHF5rEPejPZx9Gn1smb8sRU88rwjE4TWjDKWRzeOShGaBXt6uDn_NEfduo3y7CzJo6dAuurIsV__6V5_lFBFu5oPF1WLQTeerMk6PB-lQM5I9feR3_t3Y34FoDS91RbUc34ZIubsHWObLC2_DCDh1V1uxGu2-_4Djk2tj1jIHWXRZYotBWlItQFgtK49qBaWXTvu7AfPzm497Ua1Zf8CRCJOpxxAbKz2RmZBJjT-cmSLTGaFooKu1SNZxg9KWoL7iKfBUJgo4tkqlJVCAyn0Z3oVeUhb4HLg91wmWGSFOlMRGE08w3EV5uQuqbMHZgt9UEkw01uV0hI2dtiIJKY5XSHHjSia5qPo7fCe1U6uwk-PGRTWAjCTuYTdjBZD98tU-mbOHAdqtv1vTdE2Z_HCKKwc2Bx91p7HX2VwovdLlGGULQtWOF8N13ajvpHmYJu18vP41YeXzIUKOsVqYDTzs7-tur71YW9mcJtjcZVwf3_-mGD-AqYrykztHchh7ahH6IOOpUPGr6zE-ceBoi priority: 102 providerName: Unpaywall |
| Title | Compressive Image Reconstruction in Reduced Union of Subspaces |
| URI | https://api.istex.fr/ark:/67375/WNG-WGL2BL7H-X/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fcgf.12539 https://www.proquest.com/docview/1690185555 https://www.proquest.com/docview/1778017744 https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119639 http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119639 |
| UnpaywallVersion | submittedVersion |
| Volume | 34 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1467-8659 dateEnd: 20241006 omitProxy: true ssIdentifier: ssj0004765 issn: 0167-7055 databaseCode: ABDBF dateStart: 19980301 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1467-8659 dateEnd: 20241006 omitProxy: false ssIdentifier: ssj0004765 issn: 0167-7055 databaseCode: ADMLS dateStart: 19980101 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVEBS databaseName: The EBSCOhost Business Source Ultimate customDbUrl: eissn: 1467-8659 dateEnd: 20241006 omitProxy: false ssIdentifier: ssj0004765 issn: 0167-7055 databaseCode: AKVCP dateStart: 19980301 isFulltext: true titleUrlDefault: https://search.ebscohost.com/login.aspx?authtype=ip,uid&profile=ehost&defaultdb=bsu providerName: EBSCOhost – providerCode: PRVWIB databaseName: Wiley Online Library - Core collection (SURFmarket) issn: 0167-7055 databaseCode: DR2 dateStart: 19970101 customDbUrl: isFulltext: true eissn: 1467-8659 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004765 providerName: Wiley-Blackwell |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1ZT9tAEB4heAAeWiitmhaQewjx4sj32qpUKSEkaUUjVDUlSJVWe3hRROpEQNrCr2fGF6SiVVU_-RjL4905vrVnvwV46zKpXEzMth8LY6OFRHasAmV7xjVMxp5wE5oo_GkQ9YfBx1E4WoJ31VyYgh-i_uBGnpHHa3JwIS_vObk6M03Mzj5N3nP9iHjzO5_vqKMCFoUVrzcxxpSsQlTFU9-5kItWqFl_LQLNgjx0HVbn2Uxc_xSTySKGzZNQ9zF8q9Qvak_Om_Mr2VQ3vzE7_uf7bcCjEpxarcKaNmEpzZ7A-j3Kwi14TwEkr539kVofvmM0smgEe8dDa40zPKPRYrSFgBZPTI1F4WlGxV9PYdg9_HLQt8s1GGyFQCmxBSIE7cQqNioM0N-FccM0xTG11ImiBWsEwzGYThwptO9oXzJMb76KTKhdGTuJ_wyWs2mWPgdLeGkoVIx4U0cBk0wksWN8vN14iWO8oAH7VW9wVRKU0zoZE14NVLBJeN4kDXhdi84KVo6HhPbyLq0lxMU5lbGxkJ8Mevykd-S1j1ifjxqwXfU5Lz34ktPvQ8QyuDXgVX0ZfY9-qIgsnc5RhjFM8PhCqPteYSv1w4i2uzP-2uLTizM-Gc9ROYx1qNSb2pb-pvp-bhp_luAHvW6-8-LfRV_CGsK9sCjX3IZlNIx0ByHVldyFlVa70-7u5j6ER8PBcev0FsdgHOM |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1JT9tAFH6icKAcKN3UAKXuIsTFkfexpaoSS5PQhhwqKLlUo1k8KCJ1IiDQ9tf3vfECqdqqqk_W-I08y1u-8Tx_A_DGZ1L5GJjdMBXGRQ1J3FRFyg2Mb5hMA-Fn9KPw0SDpnUQfhvFwAd7W_8KU_BDNBzeyDOuvycDpg_QdK1dnpo3hOczuwZLdnyNI9OmWPCpiSVwzexNnTMUrRHk8TdW5aLREA_ttHmqW9KErsDwrpuL7jRiP51GsDUOdB_Cl7kCZfXLenl3JtvrxC7fj__ZwDVYrfOrslgr1EBby4hGs3GEtfAzvyIfY9Nnr3Dn8ig7JoUXsLRWtMyqwRKPSaAcxLRZMjEMeakr5X0_gpPP-eL_nVscwuAqxUuYKBAnaS1VqVByhyQvjx3mOy2qpM0Vn1giGyzCdeVLo0NOhZBjhQpWYWPsy9bLwKSwWkyJ_Bo4I8lioFCGnTiImmchSz4RY3QSZZ4KoBTv1dHBVcZTTURljXq9VcEi4HZIWvGpEpyUxx--Etu2cNhLi4pwy2VjMTwddftrtB3t91uPDFmzWk84rI77ktIOIcAavFrxsHqP50Z6KKPLJDGUYwxiPHcK2b5fK0ryMmLsPRp93-eTijI9HM2wcujts1OtGmf7W9B2rG3-W4Pvdjr1Z_3fRF7DcOz7q8_7h4OMG3Ef0F5fZm5uwiEqSP0eEdSW3rCH9BKkXHd8 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1ZT9tAEB5RkFp4KPQSaSm4hxAvjnyvrUqVuJLQplFVNSUPlVZ7eFFE6kSUFMqvZ8YXpAJU4SfLHsuzu3N86x1_C_DeZVK5mJhtPxbGRguJ7FgFyvaMa5iMPeEm9KPwl17U6QefBuFgDj5U_8IU_BD1BzfyjDxek4NPtLnm5OrINDE7-8kDWAgiTJOEiL5dcUcFLAorYm-ijClphaiMp350JhktUL-ezyLNgj10CR5Ns4n4eyZGo1kQm2eh1jL8rPQvik-Om9NT2VQX_1A73rOBK_C4RKfWdmFOT2AuzZ7C0jXOwmfwkSJIXjz7J7UOfmE4smgKe0VEaw0zvKLRZLSFiBYvjI1F8WlC1V_Pod_a_77bsctNGGyFSCmxBUIE7cQqNioM0OGFccM0xUm11ImiHWsEw0mYThwptO9oXzLMb76KTKhdGTuJ_wLms3GWroIlvDQUKkbAqaOASSaS2DE-Pm68xDFe0ICtajS4KhnKaaOMEa9mKtglPO-SBrytRScFLcdNQpv5kNYS4uSY6thYyA97bX7Y7no7XdbhgwasVWPOSxf-zWn9EMEMHg14U99G56MVFZGl4ynKMIYZHhuEum8WtlK_jHi794Y_tvn45IiPhlNUDoMdKvWutqW7VN_KTeN2Cb7bbuUnL_9fdAMeft1r8e5B7_MrWEToFxalm2swjzaSvkZ4dSrXcze6BDcDHI4 |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Zb9NAEB5B8gB94EYYCjKHqr448r3eSIBCIQmoRDwQGp5We1ZRjR21TTh-PbO-2opDYMmStR4fuzO78409-y3As4AIGaBj9qKMGw8tJPUyGUsvNIEhIgt5QO1E4fezdDqP3y2SxdmnC2y-Jskdg80y3-iXWPIc92EhiuGJHubL9VAtN9wLrOnQy9BHF5rEPejPZx9Gn1smb8sRU88rwjE4TWjDKWRzeOShGaBXt6uDn_NEfduo3y7CzJo6dAuurIsV__6V5_lFBFu5oPF1WLQTeerMk6PB-lQM5I9feR3_t3Y34FoDS91RbUc34ZIubsHWObLC2_DCDh1V1uxGu2-_4Djk2tj1jIHWXRZYotBWlItQFgtK49qBaWXTvu7AfPzm497Ua1Zf8CRCJOpxxAbKz2RmZBJjT-cmSLTGaFooKu1SNZxg9KWoL7iKfBUJgo4tkqlJVCAyn0Z3oVeUhb4HLg91wmWGSFOlMRGE08w3EV5uQuqbMHZgt9UEkw01uV0hI2dtiIJKY5XSHHjSia5qPo7fCe1U6uwk-PGRTWAjCTuYTdjBZD98tU-mbOHAdqtv1vTdE2Z_HCKKwc2Bx91p7HX2VwovdLlGGULQtWOF8N13ajvpHmYJu18vP41YeXzIUKOsVqYDTzs7-tur71YW9mcJtjcZVwf3_-mGD-AqYrykztHchh7ahH6IOOpUPGr6zE-ceBoi |
| 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=Compressive+Image+Reconstruction+in+Reduced+Union+of+Subspaces&rft.jtitle=Computer+graphics+forum&rft.au=Miandji%2C+Ehsan&rft.au=Kronander%2C+Joel&rft.au=Unger%2C+Jonas&rft.date=2015-05-01&rft.issn=1467-8659&rft.volume=34&rft.issue=2&rft.spage=33&rft_id=info:doi/10.1111%2Fcgf.12539&rft.externalDocID=oai_DiVA_org_liu_119639 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0167-7055&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0167-7055&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0167-7055&client=summon |