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All Technical Committee Conferences (All Years)
| 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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