10 June 2013 Super-resolution algorithm exploiting multiple dictionaries based on local image structures
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Optical Engineering, 52(6), 067002 (2013). doi:10.1117/1.OE.52.6.067002
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 example-based superresolution algorithm based on neighbor embedding.
© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Il-Hyun Choi, Kyoung Won Lim, Byung Cheol Song, "Super-resolution algorithm exploiting multiple dictionaries based on local image structures," Optical Engineering 52(6), 067002 (10 June 2013). https://doi.org/10.1117/1.OE.52.6.067002

Associative arrays


Reconstruction algorithms

Super resolution

Image enhancement

Optical engineering

Fluctuations and noise

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