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http://dx.doi.org/10.9728/dcs.2014.15.6.737

Super Resolution Technique Through Improved Neighbor Embedding  

Eum, Kyoung-Bae (Kunsan University, Dept. of Computer and Information Eng.)
Publication Information
Journal of Digital Contents Society / v.15, no.6, 2014 , pp. 737-743 More about this Journal
Abstract
For single image super resolution (SR), interpolation based and example based algorithms are extensively used. The interpolation algorithms have the strength of theoretical simplicity. However, those algorithms are tending to produce high resolution images with jagged edges, because they are not able to use more priori information. Example based algorithms have been studied in the past few years. For example based SR, the nearest neighbor based algorithms are extensively considered. Among them, neighbor embedding (NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the sizes of local training sets are always too small. So, NE algorithm is weak in the performance of the visuality and quantitative measure by the poor generalization of nearest neighbor estimation. An improved NE algorithm with Support Vector Regression (SVR) was proposed to solve this problem. Given a low resolution image, the pixel values in its high resolution version are estimated by the improved NE. Comparing with bicubic and NE, the improvements of 1.25 dB and 2.33 dB are achieved in PSNR. Experimental results show that proposed method is quantitatively and visually more effective than prior works using bicubic interpolation and NE.
Keywords
Super Resolution; Improved NE; Support Vector Regression;
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