Open AccessFast Super‐Resolution Algorithm Based on Dictionary Size Reduction Using k ‐Means Clustering
Author(s) -
Jeong ShinCheol,
Song Byung Cheol
Publication year - 2010
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.10.0109.0637
Subject(s) - cluster analysis , computer science , reduction (mathematics) , computation , algorithm , computational complexity theory , matching (statistics) , dictionary learning , artificial intelligence , resolution (logic) , canopy clustering algorithm , pattern recognition (psychology) , correlation clustering , sparse approximation , mathematics , statistics , geometry
This paper proposes a computationally efficient learning‐based super‐resolution algorithm using k ‐means clustering. Conventional learning‐based super‐resolution requires a huge dictionary for reliable performance, which brings about a tremendous memory cost as well as a burdensome matching computation. In order to overcome this problem, the proposed algorithm significantly reduces the size of the trained dictionary by properly clustering similar patches at the learning phase. Experimental results show that the proposed algorithm provides superior visual quality to the conventional algorithms, while needing much less computational complexity.