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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 115  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
IMQ 2022-05-27
14:25
Tokyo   Classification-ESRGAN -- Synthesis of super-resolution images based on subject categorization --
Jingan Liu, Atsumu Harada, Naiwala P. Chandrasiri (Kogakuin Univ.) IMQ2022-3
In recent years, super-resolution techniques have been significantly developed based on deep learning. In particular, GA... [more] IMQ2022-3
pp.12-17
RCS, SR, SRW
(Joint)
2022-03-04
09:55
Online Online Low-overhead Beam and Power Allocation Using Deep Learning for mmWave Networks
Yuwen Cao, Tomoaki Ohtsuki (Keio Univ.) RCS2021-284
In this report, we develop a novel deep learning (DL)-based hybrid beam and power allocation approach for multiuser mill... [more] RCS2021-284
pp.159-163
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] 2022-02-21
15:20
Online Online Resolution in 3D super-resolution projection using single-lens spatial cross modulation method
Xinruinan Zhang, Atsushi Okamoto (Hokkaido Univ.), Hisatoshi Funakoshi (Gifu Univ.), Shuanglu Zhang, Akihisa Tomita (Hokkaido Univ.)
Spatial cross modulation (SCMM) can regenerate optical complex amplitudes by combining a single spatial light modulator ... [more]
MI 2022-01-26
13:00
Online Online Relationship between Image Quality and Learning Effect in Color Laparoscopic Images Generation by Generative Adversarial Networks
Norifumi Kawabata (Hokkaido Univ.), Toshiya Nakaguchi (Chiba Univ.) MI2021-59
Improving of personal computer performance, it is possible for healthcare workers and related researchers to support for... [more] MI2021-59
pp.59-64
NLP 2021-12-18
15:15
Oita J:COM Horuto Hall OITA On Weight Filter Generation Using an Attention Module in a Super-Resolution Method
Keitaro Otani, Hidehiro Nakano (Tokyo City Univ.) NLP2021-66
In recent years, the development of computer technology has led to an increase in the number of systems that require lar... [more] NLP2021-66
pp.104-109
PRMU 2021-12-16
11:00
Online Online Low-Resolution Iris Recognition with Image Super-Resolution for arbitrary magnification
Tsubasa Bora (UEC), Takahiro Toizumi, Yuho Shoji, Yuka Ogino, Masato Tsukada (NEC), Masatsugu Ichino (UEC) PRMU2021-26
A low-resolution iris image reduces iris recognition accuracy. Some conventional researches tackle low-resolution iris r... [more] PRMU2021-26
pp.13-18
IMQ 2021-10-22
13:45
Osaka Osaka Univ. A Tiny Convolutional Neural Network for Image Super-Resolution
Kazuya Urazoe, Nobutaka Kuroki, Yu Kato, Shinya Ohtani (Kobe Univ.), Tetsuya Hirose (Osaka Univ.), Masahiro Numa (Kobe Univ.) IMQ2021-7
This paper surveys three techniques for reducing computational costs of convolutional neural network (CNN) for image sup... [more] IMQ2021-7
pp.2-7
MI 2021-05-17
14:40
Online Online MR super-resolution based on signal-image domain learning using phase scrambling Fourier transform imaging
Kazuki Yamato, Hiromichi Wakatsuki, Satoshi Ito (Utsunomiya Univ.) MI2021-6
In the phase-scrambling Fourier transform (PSFT) imaging, the signals not sampled during imaging can be extrapolated and... [more] MI2021-6
pp.14-19
MI 2021-03-17
11:00
Online Online Optimal Design and Quality Assessment of Color Laparoscopic Super-Resolution Image by Generative Adversarial Networks
Norifumi Kawabata (Tokyo Univ. of Science), Toshiya Nakaguchi (Chiba Univ.) MI2020-91
The Generative Adversarial Networks (GAN) is unsupervised learning enabled to transform according to data characteristic... [more] MI2020-91
pp.186-190
MI 2021-03-17
14:15
Online Online Super-resolution of thoracic CT volumes using high-frequency learning
Ryosuke Kawai, Atsushi Saito (TUAT), Shoji Kido (Osaka Univ), Kunihiro Inai, Hirohiko Kimura (Fukui Univ), Akinobu Shimizu (TUAT) MI2020-97
We report the results of super-resolution using a new network model. Specifically, the reconstructed image is represente... [more] MI2020-97
pp.218-219
MBE, NC
(Joint)
2020-12-18
14:50
Online Online Super resolution for sea surface temperature with CNN and GAN
Tomoki Izumi, Motoki Amagasaki, Kei Ishida, Masato Kiyama (Kumamoto Univ.) NC2020-28
In this paper, we use the deep neural networks (DNN)-based single image super-resolution (SISR) method for the super res... [more] NC2020-28
pp.1-6
IE, CS, IPSJ-AVM, ITE-BCT [detail] 2020-11-27
15:00
Online Online Comparative Study of Channel Estimation using Deep-learning based Super-resolution
Daiki Maruyama, Kenji Kanai, Jiro Katto (Waseda Univ.) CS2020-73 IE2020-32
Recently, application of deep learning into communication systems are getting lots of attention to researchers. Especial... [more] CS2020-73 IE2020-32
pp.39-44
MICT, MI 2020-11-04
15:50
Online Online [Short Paper] An Experimental Study on Color Laparoscopic High-Definition Video Quality Assessment Including Super-resolution
Norifumi Kawabata (Tokyo Univ. of Science), Toshiya Nakaguchi (Chiba Univ.) MICT2020-19 MI2020-45
After considering medical image analysis support, it is one of importance factors to assess high-definition image qualit... [more] MICT2020-19 MI2020-45
pp.60-61
ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2020-02-27
16:35
Hokkaido Hokkaido Univ.
(Cancelled but technical report was issued)
A Study on Region Segmentation of Color Laparoscopic Images after Contrast Enhancement Including Super-Resolution CNN by Image Regions
Norifumi Kawabata (Tokyo Univ. of Science), Toshiya Nakaguchi (Chiba Univ.)
As one of image pre-processing method to detect, recognize, and estimate lesion or characteristic region in medical imag... [more]
MI 2020-01-30
10:50
Okinawa OKINAWAKEN SEINENKAIKAN [Short Paper] Study of image quality improvement technique using deep learning for nuclear medicine images
Masaya Momiuchi, Takeshi Hara (Gifu Univ), Tetsuro Katafuchi (Gifu Univ of Medical Science), Masaki Matsusako (St. Luke's Hospital), Hiroshi Fujita (Gifu Univ) MI2019-102
Spatial resolutions in nuclear medical imaging are not equivalent to ordinary medical images such as CT or MR modalities... [more] MI2019-102
pp.165-168
IBISML 2020-01-10
11:25
Tokyo ISM Real-time 3D localization of multifocal plane microscopy using deep neural network
Toshimitsu Aritake (Waseda Univ.), Hideitsu Hino (ISM), Shigeyuki Namiki, Daisuke Asanuma, Kenzo Hirose (The Univ. of Tokyo), Noboru Murata (Waseda Univ.) IBISML2019-29
(To be available after the conference date) [more] IBISML2019-29
pp.75-81
SANE 2019-08-23
14:25
Tokyo Electric Navigation Research Institute Super-resolution Scattering Direction Estimation for Circular SAR Imagery
Takuma Watanabe, Daisuke Ogawa, Yasuyuki Oishi (Fujitsu) SANE2019-41
Circular synthetic aperture radar (CSAR) is a microwave imaging technique which employs a circular flight trajectory of ... [more] SANE2019-41
pp.37-42
IE 2019-06-21
13:00
Okinawa   SUPER-RESOLUTION BASED ON BACK-PROJECTION OF INTERPOLATED IMAGE
Sawiya Kiatpapan, Takuro Yamaguchi, Masaaki Ikehara (Keio Univ.) IE2019-17
Image upscaling to obtain high quality digital image is one of the active research topics as it is applicable in the con... [more] IE2019-17
pp.1-5
EA, SIP, SP 2019-03-14
13:30
Nagasaki i+Land nagasaki (Nagasaki-shi) [Poster Presentation] Image Super-Resolution via Generative Adversarial Network Considering Objective Quality
Hiroya Yamamoto, Daichi Kitahara, Akira Hirabayashi (Ritsumeikan Univ.) EA2018-115 SIP2018-121 SP2018-77
We propose a super-resolution method based on a conventional technique using the generative adversarial network (GAN). T... [more] EA2018-115 SIP2018-121 SP2018-77
pp.93-98
IBISML 2019-03-06
13:00
Tokyo RIKEN AIP Magnetic Resonance Angiography Image Restoration by Super Resolution based on Deep Learning
Shizen Kitazaki, Masanori Kawakita, Yutaka Jitumatu (Kyushu Univ.), Shigehide Kuhara (Kyorin Univ.), Akio Hiwatashi, Jun'ichi Takeuchi (Kyushu Univ.) IBISML2018-114
Magnetic Resonance Imaging (MRI) is one of the powerful techniques to acquire in vivo information. However, to obtain a ... [more] IBISML2018-114
pp.65-72
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