Paper Abstract and Keywords |
Presentation |
2020-02-28 15:10
Unpaired Learning for Noise-free, Scale Invariant, and Interpretable Image Enhancement Satoshi Kosugi, Toshihiko Yamasaki (Univ. of Tokyo) |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into enhanced images in the absence of input-output image pairs. Our method is based on generative adversarial networks (GANs), but instead of simply generating images with a neural network, we enhance images utilizing image editing software to achieve noise-free, scale invariant, and interpretable image enhancement. To incorporate image editing software into a GAN, we propose a reinforcement learning framework. We apply the proposed method to photo enhancement and face beautification and demonstrate that the proposed method achieves the best performance. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
image enhancement / unpaired learning / reinforcement learning / generative adversarial network / / / / |
Reference Info. |
ITE Tech. Rep. |
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Conference Information |
Committee |
HI IEICE-IE IEICE-ITS MMS ME AIT |
Conference Date |
2020-02-27 - 2020-02-28 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Hokkaido Univ. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Image Processing, etc. |
Paper Information |
Registration To |
IEICE-IE |
Conference Code |
2020-02-HI-IE-ITS-MMS-ME-AIT |
Language |
English |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Unpaired Learning for Noise-free, Scale Invariant, and Interpretable Image Enhancement |
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image enhancement |
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unpaired learning |
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reinforcement learning |
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generative adversarial network |
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1st Author's Name |
Satoshi Kosugi |
1st Author's Affiliation |
the University of Tokyo (Univ. of Tokyo) |
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Toshihiko Yamasaki |
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the University of Tokyo (Univ. of Tokyo) |
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Speaker |
Author-1 |
Date Time |
2020-02-28 15:10:00 |
Presentation Time |
15 minutes |
Registration for |
IEICE-IE |
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Volume (vol) |
vol.44 |
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