Presentation 2013-03-14
Self-learning Super Resolution by l_2-norm
Daiki AZUMA, Kazu MISHIBA, Masaaki IKEHARA,
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Abstract(in English) In this paper, we propose a single image super resolution technique by l_2 approximation without any training. Recently it has been proposed that image super resolution by using sparse representation. However it has to make a dictionary from tremendous low and high resolution image pairs to apply in any kinds of inputs. Moreover this algorithm takes a long time to train and requires the dictionaries to learn in advance. In our method, the high- and low- resolution dictionaries are made from random selected patch pairs of input and its reduced image, respectively. Then the input image is approximated by low-resolution dictionary in the sense of l_2 and its coefficients are used to reconstruct the high-resolution image. Therefore the proposed method does not need any training to make dictionaries and any time consuming computation for approximations. In some example, our method has comparable quality to the conventional method.
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Keyword(in English) super resolution / sparse coding / l_2-norm minimization / self-similarity
Paper # CAS2012-106,SIP2012-137,CS2012-112
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Committee CAS
Conference Date 2013/3/7(1days)
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Registration To Circuits and Systems (CAS)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Self-learning Super Resolution by l_2-norm
Sub Title (in English)
Keyword(1) super resolution
Keyword(2) sparse coding
Keyword(3) l_2-norm minimization
Keyword(4) self-similarity
1st Author's Name Daiki AZUMA
1st Author's Affiliation Department of Electronics and Electrical Engineering, Keio University()
2nd Author's Name Kazu MISHIBA
2nd Author's Affiliation Department of Electrical and Electronic Engineering, Tottori University
3rd Author's Name Masaaki IKEHARA
3rd Author's Affiliation Department of Electronics and Electrical Engineering, Keio University
Date 2013-03-14
Paper # CAS2012-106,SIP2012-137,CS2012-112
Volume (vol) vol.112
Number (no) 484
Page pp.pp.-
#Pages 5
Date of Issue