教師資料查詢 | 類別: 期刊論文 | 教師: 珽 CHING-TING TU (瀏覽個人網頁)

標題:Super-Resolution Based on Clustered Examples
學年104
學期2
出版(發表)日期2016/03/18
作品名稱Super-Resolution Based on Clustered Examples
作品名稱(其他語言)
著者Ching Ting Tu; Hsiau Wen Lin; Hwei-Jen Lin; Yue Shen Li
單位
出版者
著錄名稱、卷期、頁數International Journal of Pattern Recognition and Artificial Intelligence 30(6), p.1655015 (15 pages)
摘要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. 275–282]. 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’s 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. T. Roweis and L. K. Saul, Nonlinear dimensionality reduction by locally linear embedding, Science 290(5500) (2000) 2323–2326] 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.
關鍵字Super-resolution;locally linear embedding (LLE);K-means++++ clustering;interpolation;learning-based method;example-based method;degradation
語言英文
ISSN0218-0014;1793-6381
期刊性質國外
收錄於
產學合作
通訊作者
審稿制度
國別新加坡
公開徵稿
出版型式,電子版,紙本
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