期刊論文

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
ISSN 0218-0014;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