Locality preserving constraints for super-resolution with neighbor embedding

Authors:
Bo Li;Hong Chang;Shiguang Shan;Xilin Chen
Affiliations:
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing, China and Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing, China and Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing, China and Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

In this paper, we revisit the manifold assumption which has been widely adopted in the learning-based image superresolution. The assumption states that point-pairs from the high-resolution manifold share the local geometry with the corresponding low-resolution manifold. However, the assumption does not hold always, since the one-to-multiple mapping from LR to HR makes neighbor reconstruction ambiguous and results in blurring and artifacts. To minimize the ambiguous, we utilize Locality Preserving Constraints (LPC) to avoid confusions through emphasizing the consistency of localities on both manifolds explicitly. The LPC are combined with a MAP framework, and realized by building a set of cell-pairs on the coupled manifolds. Finally, we propose an energy minimization algorithm for the MAP with LPC which can reconstruct high quality images compared with previous methods. Experimental results show the effectiveness of our method.