DocumentCode :
2639833
Title :
New learning-based super resolution utilizing total variation regularization method
Author :
Suzuki, Shotaro ; Yoshikawa, Akihiro ; Goto, Tomio ; Hirano, Satoshi ; Sakurai, Masaru
Author_Institution :
Dept. of Comput. Sci. & Eng., Nagoya Inst. of Technol., Nagoya, Japan
fYear :
2011
fDate :
9-12 Jan. 2011
Firstpage :
253
Lastpage :
254
Abstract :
In this paper, we propose a new learning-based approach for super resolution image reconstruction utilizing total variation regularization method. By using the total variation (TV) regularization decomposition, we obtain the structure component which consists of edge component and the texture component which does not include edge component of the image. We use the texture component for the learning-based method instead of high frequency component. The experimental results show improved performance, short computational time, and robustness to the noise compared with the conventional learning-based method.
Keywords :
image reconstruction; learning (artificial intelligence); high frequency component; learning-based method; learning-based super resolution; super resolution image reconstruction; total variation regularization decomposition; total variation regularization method; Image edge detection; Image resolution; Interpolation; Learning systems; Noise; TV; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Consumer Electronics (ICCE), 2011 IEEE International Conference on
Conference_Location :
Las Vegas, NV
ISSN :
2158-3994
Print_ISBN :
978-1-4244-8711-0
Type :
conf
DOI :
10.1109/ICCE.2011.5722568
Filename :
5722568
Link To Document :
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