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Paper Abstract and Keywords
Presentation 2007-04-25 14:00
Image Resolutuin compression based on Retina Model using DT-CNN
Yoshiei Tanaka, Hisashi Aomori (Sophia Univ.), Tsuyoshi Otake (Tamagawa Univ.), Nobuaki Takahashi (IBM Japan,), Mamoru Tanaka (Sophia Univ.) NLP2007-4
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, image resolution compression based on retina model using DT-CNN is proposed.
By using sigma-delta modulator by CNN, the input image can be converted into digital pulse sequences, and the image can be reconstructed.
Human has 100 million retinal cells or more, and the input signal via the retina is sent to the cerebrum visual field.
The signal from there is transmitted to the cerebrum visual field through the optic nerve fiber of about one million.
In a word, the resolution of input image is compressed, and converted into binary digital purse sequences in the system from the retina to the cerebrum visual field.
These binary digital pulse sequences are sent to the cerebrum visual field.
The transmitted binary digital pulse sequences are reconstructed in the brain finally.
That is, a model from the retina to the cerebrum can be achieved by using CNN.
The experimental results show that a good quality reconstruction resolution compressed image was able to be obtained, and the image resolution compression by CNN based on the retina model was able to be achieved.
Keyword (in Japanese) (See Japanese page) 
(in English) Retina model / Cellular Neural Network / Resolution compression / / / / /  
Reference Info. IEICE Tech. Rep., vol. 107, no. 21, NLP2007-4, pp. 19-24, April 2007.
Paper # NLP2007-4 
Date of Issue 2007-04-18 (NLP) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee NLP  
Conference Date 2007-04-25 - 2007-04-25 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NLP 
Conference Code 2007-04-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Image Resolutuin compression based on Retina Model using DT-CNN 
Sub Title (in English)  
Keyword(1) Retina model  
Keyword(2) Cellular Neural Network  
Keyword(3) Resolution compression  
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1st Author's Name Yoshiei Tanaka  
1st Author's Affiliation Sophia University (Sophia Univ.)
2nd Author's Name Hisashi Aomori  
2nd Author's Affiliation Sophia University (Sophia Univ.)
3rd Author's Name Tsuyoshi Otake  
3rd Author's Affiliation Tamagawa University (Tamagawa Univ.)
4th Author's Name Nobuaki Takahashi  
4th Author's Affiliation IBM Japan, Ltd. (IBM Japan,)
5th Author's Name Mamoru Tanaka  
5th Author's Affiliation Sophia University (Sophia Univ.)
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Speaker Author-5 
Date Time 2007-04-25 14:00:00 
Presentation Time 30 minutes 
Registration for NLP 
Paper # NLP2007-4 
Volume (vol) vol.107 
Number (no) no.21 
Page pp.19-24 
#Pages 6 
Date of Issue 2007-04-18 (NLP) 

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