期刊論文
| 學年 | 104 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2016-03-16 |
| 作品名稱 | Super-Resolution Based on Clustered Examples |
| 作品名稱（其他語言） | |
| 著者 | Tu, Ching-Ting; Lin, Hsiau-Wen; Lin, Hwei-Jen; Li, Yue-Sheng |
| 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 | en |
| ISSN | 0218-0014;1793-6381 |
| 期刊性質 | 國外 |
| 收錄於 | SCI EI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | SGP |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/109572 ) |