Super-resolution Algorithm Exploiting Multiple Dictionaries Based on Local Image Structures

Title
Super-resolution Algorithm Exploiting Multiple Dictionaries Based on Local Image Structures
Authors
송병철
Keywords
example-based superresolution; learning-based superresolution; dictionary; sharpness enhancement; local structure analysis
Issue Date
2013
Publisher
OPTICAL ENGINEERING
Series/Report no.
OPTICAL ENGINEERING ; Vol52 no.6 Startpage 067002 Endpage 067002
Abstract
We propose an example-based superresolution algorithm that adaptively exploits multiple dictionaries based on local image structures. Noise, irregularities, and blurred textures are noticeable artifacts in the reconstructed image due to a shortage of relevant examples and false exploration in the dictionary. These artifacts are emphasized during successive enhancement. We alleviate the artifacts by constructing multiple dictionaries coupled with different sharpness levels during the learning phase. We exploit these dictionaries adaptively based on local image structures during the synthesis phase. Experimental results show that the proposed algorithm provides more detailed images with significantly reduced artifacts while consuming only 8.6% of storage capacity and 0.25% of CPU running time in comparison with a typical examplebased superresolution algorithm based on neighbor embedding.
URI
http://dspace.inha.ac.kr/handle/10505/34308
ISSN
0091-3286
Appears in Collections:
College of Engineering(공과대학) > Electronic Engineering (전자공학) > Local Access Journals, Paper, Reports (전자공학 논문, 보고서)

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