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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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 |
| OFT, OCS, IEE-CMN, ITE-BCT [detail] | 2021-11-19 10:50 |
Online | Online | Super-simplified optical correlation-domain reflectometry Takaki Kiyozumi, Tomoya Miyamae (YNU), Kohei Noda (Tokyo Tech/YNU), Heeyoung Lee (SIT), Kentaro Nakamura (Tokyo Tech), Yosuke Mizuno (YNU) OFT2021-53 |
Optical fiber reflectometry has been widely studied as a method for diagnosing the soundness of fiber networks. Of numer... [more] | OFT2021-53 pp.13-16 |
| 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 |
| PRMU | 2021-08-26 16:00 |
Online | Online | A Study of Low-Resolution Iris Biometrics using Single Image Super-Resolution Tsubasa Bora, Daisuke Uenoyama (UEC), Takahiro Toizumi, Yuka Ogino, Masato Tsukada (NEC), Masatsugu Ichino (UEC) PRMU2021-14 |
It requires a high-quality iris image in general, which means that subject must look into the camera, which is highly in... [more] | PRMU2021-14 pp.42-47 |
| IE, SIP, BioX, ITE-IST, ITE-ME [detail] | 2021-06-03 16:30 |
Online | Online | A Study on Compression Artifacts Reduction by a Super-Resolution Network Shion Komatsu, Joi Shimizu, Heming Sun, Jiro Katto (Waseda Univ.) SIP2021-4 BioX2021-4 IE2021-4 |
Due to the huge amount of information in digital video signals, video compression is used to reduce the amount of inform... [more] | SIP2021-4 BioX2021-4 IE2021-4 pp.15-20 |
| MI | 2021-05-17 11:00 |
Online | Online | [Short Paper]
Watershed-based Alveoli Segmentation from Micro-focus X-ray CT Volumes of Dissected Human Lungs Takeru Shiina, Hirohisa Oda, Tong Zheng, Shota Nakamura, Masahiro Oda, Kensaku Mori (Nagoya Univ.) MI2021-3 |
We propose a segmentation method of the alveoli from μCT volumes. The peripheral lung mainly consists of tiny spherical ... [more] | MI2021-3 pp.9-10 |
| 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 |
| MVE, IMQ, IE, CQ (Joint) [detail] |
2021-03-03 13:00 |
Online | Online | [Invited Talk]
Delicious life with Super Hi-Vision Yoshiaki Shishikui (Meiji Univ.) CQ2020-116 |
In 4K / 8K Super Hi-Vision, which started broadcasting two years ago, the resolution, color gamut, dynamic range, etc. h... [more] | CQ2020-116 pp.44-48 |
| NC, NLP (Joint) |
2021-01-21 12:05 |
Online | Online | Examination of precipitation estimation using atmospheric variables Takanori Ito, Motoki Amagasaki, Kei Ishida, Masato Kiyama, Masahiro Iida (GSST Kumamoto University) NC2020-34 |
In this paper, we developed a model for SR using ConvLSTM to improve the resolution of precipitation data. In the relat... [more] |
NC2020-34 pp.13-17 |
| 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 |
| SCE | 2019-10-09 16:15 |
Miyagi | Design of High Timing resolution Time-to-Digital Convertor for Time-Resolving Photon Detection System using SNSPD Hiroaki Myoren, Ryotaro Kamiya, Kota Aita, Masato Naruse, Tohru Taino (Saitama Univ.), Lin Kang, Jian Chen, Peiheng Wu (Nanjing Univ.) SCE2019-26 |
Superconducting nanowire single photon detectors (SNSPDs) , those have a low dark count rate characteristics, fast and l... [more] | SCE2019-26 pp.23-26 |
|
| 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 |
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