{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T09:42:57Z","timestamp":1648892577034},"reference-count":15,"publisher":"World Scientific Pub Co Pte Lt","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2016,7]]},"abstract":"<jats:p> In this paper, we propose an improved version of the neighbor embedding super-resolution (SR) algorithm proposed by Chang et al. [Super-resolution through neighbor embedding, in Proc. 2004 IEEE Computer Society Conf. Computer Vision and Pattern Recognition(CVPR), Vol. 1 (2004), pp. 275\u2013282]. The neighbor embedding SR algorithm requires intensive computational time when finding the K nearest neighbors for the input patch in a huge set of training samples. We tackle this problem by clustering the training sample into a number of clusters, with which we first find for the input patch the nearest cluster center, and then find the K nearest neighbors in the corresponding cluster. In contrast to Chang\u2019s method, which uses Euclidean distance to find the K nearest neighbors of a low-resolution patch, we define a similarity function and use that to find the K most similar neighbors of a low-resolution patch. We then use local linear embedding (LLE) [S.\u00a0T.\u00a0Roweis and L.\u00a0K.\u00a0Saul, Nonlinear dimensionality reduction by locally linear embedding, Science 290(5500) (2000) 2323\u20132326] to find optimal coefficients, with which the linear combination of the K most similar neighbors best approaches the input patch. These coefficients are then used to form a linear combination of the K high-frequency patches corresponding to the K respective low-resolution patches (or the K most similar neighbors). The resulting high-frequency patch is then added to the enlarged (or up-sampled) version of the input patch. Experimental results show that the proposed clustering scheme efficiently reduces computational time without significantly affecting the performance. <\/jats:p>","DOI":"10.1142\/s0218001416550156","type":"journal-article","created":{"date-parts":[[2016,2,16]],"date-time":"2016-02-16T04:42:13Z","timestamp":1455597733000},"page":"1655015","source":"Crossref","is-referenced-by-count":1,"title":["Super-Resolution Based on Clustered Examples"],"prefix":"10.1142","volume":"30","author":[{"given":"Ching Ting","family":"Tu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hsiau Wen","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Information Management, Chihlee Institute of Technology, New Taipei City, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hwei-Jen","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue Shen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2016,5,9]]},"reference":[{"key":"S0218001416550156BIB004","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2005.845313"},{"key":"S0218001416550156BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2010.2100401"},{"key":"S0218001416550156BIB008","doi-asserted-by":"publisher","DOI":"10.1038\/nrm2531"},{"key":"S0218001416550156BIB009","doi-asserted-by":"publisher","DOI":"10.1109\/38.988747"},{"key":"S0218001416550156BIB010","doi-asserted-by":"publisher","DOI":"10.1109\/34.476006"},{"key":"S0218001416550156BIB012","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/bxm075"},{"key":"S0218001416550156BIB013","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.811513"},{"key":"S0218001416550156BIB015","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.biochem.77.061906.092014"},{"key":"S0218001416550156BIB017","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2005.861705"},{"key":"S0218001416550156BIB018","doi-asserted-by":"publisher","DOI":"10.1109\/TASSP.1981.1163711"},{"key":"S0218001416550156BIB019","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2007.893271"},{"key":"S0218001416550156BIB020","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"S0218001416550156BIB021","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-96961-4"},{"key":"S0218001416550156BIB024","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2050625"},{"key":"S0218001416550156BIB026","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-27413-8_47"}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001416550156","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T15:37:51Z","timestamp":1565105871000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218001416550156"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,5,9]]},"references-count":15,"journal-issue":{"issue":"06","published-online":{"date-parts":[[2016,5,9]]},"published-print":{"date-parts":[[2016,7]]}},"alternative-id":["10.1142\/S0218001416550156"],"URL":"https:\/\/doi.org\/10.1142\/s0218001416550156","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,5,9]]}}}