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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 105  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
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
MI 2019-01-22
10:05
Okinawa   Super-resolution of μCT image about dissected lung tissue using Adversarial Dense U-net
Tong Zheng, Hirohisa Oda, Holger R. Roth, Masahiro Oda, Shota Nakamura (Nagoya University), Kensaku Mori (Nagoya University/NII) MI2018-61
μCT images capture three dimensional structures of tissues with a very high resolution of 100 micrometer or smaller. fin... [more] MI2018-61
pp.7-12
PN, EMT, OPE, EST, MWP, LQE, IEE-EMT [detail] 2019-01-18
14:45
Osaka Osaka University Nakanoshima Center Super-resolution for GPR Images by Deep Learning Using Generative Adversarial Networks
Jun Sonoda (NIT, Sendai), Tomoyuki Kimoto (NIT, Oita) PN2018-75 EMT2018-109 OPE2018-184 LQE2018-194 EST2018-122 MWP2018-93
Recently, deterioration of social infrastructures such as tunnels and bridges become a serious social problem. It is req... [more] PN2018-75 EMT2018-109 OPE2018-184 LQE2018-194 EST2018-122 MWP2018-93
pp.237-242
PRMU, MVE, IPSJ-CVIM [detail] 2019-01-17
16:30
Kyoto  
Ryo Nakao, Seiichi Uchida (Kyushu Univ.) PRMU2018-101 MVE2018-43
(To be available after the conference date) [more] PRMU2018-101 MVE2018-43
pp.39-44
CS, IE, IPSJ-AVM, ITE-BCT [detail] 2018-11-30
14:00
Tokushima Tokushima University (Memorial Hall of Almuni(Engineering)) IMAGE SUPER-RESOLUTION IN LOW-LIGHT SCENE USING RGB/NIR SENSOR
Takayuki Honda (TUS), Daisuke Sugimura (TC), Takayuki Hamamoto (TUS) CS2018-83 IE2018-62
We propose a method for multi-frame super-resolution (SR) of low-resolution (LR) color images taken in low-light scenes.... [more] CS2018-83 IE2018-62
pp.101-104
NLP 2018-08-08
15:00
Kagawa Saiwai-cho Campus, Kagawa Univ. Super-Resolution Reconstruction Using Adaptive Nearest Neighbor Interpolation by Iterative Back-Projection
Ryuya Ukai, Ryohei Mizutani, Yuki kawai, Teruki Uchida, Hideharu Toda (Chukyo Univ.), Tsuyoshi Otake, Masatoshi Sato (Tamagawa Univ.), Hisashi Aomori (Chukyo Univ.) NLP2018-58
The pixel density of display devices are improving, and the importance of the resolution as a criteria to determine the ... [more] NLP2018-58
pp.31-34
IMQ, HIP 2018-07-20
13:30
Hokkaido Sapporo City University, Satellite Campus [Invited Lecture] Inpainting based on low-dimensional image approximation and its applications
Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) IMQ2018-5 HIP2018-32
This paper introduces inpainting based on low-dimensional image approximation and its applications. Specifically, low-di... [more] IMQ2018-5 HIP2018-32
pp.1-4
IE 2018-06-29
09:55
Okinawa   Single Image Super-Resolution with Limited Number of Filters
Yusuke Nakahara, Takuro Yamaguchi, Masaaki Ikehara (Keio Univ.) IE2018-22
In this paper, we propose a single image super-resolution with limited number of filters based on RAISR. RAISR is well k... [more] IE2018-22
pp.7-11
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2018-06-13
15:50
Okinawa Okinawa Institute of Science and Technology 3D Super Resolution Microscopy using Convolutional Neural Network
Masaru Tanaka (Waseda Univ.), Hideitsu Hino (ISM), Shigeyuki Namiki, Daisuke Asanuma, Kenzo Hirose (The Univ. of Tokyo), Noboru Murata (Waseda Univ.) IBISML2018-11
Super-resolution microscopy is a microscopy technique with a resolution beyond the diffraction limit of light. Despite t... [more] IBISML2018-11
pp.75-80
IMQ 2018-05-25
14:15
Chiba Chibe Institute of Technology, Tsudanuma Campus Optimal Design and Coded Image Quality Assessment of the Multi-view and Super-resolution Images Based on Structure of Convolutional Neural Network
Norifumi Kawabata (Nagoya Univ.) IMQ2018-3
The image screen resolution by viewpoints is low, comparing to single-view images since there are many viewpoints for mu... [more] IMQ2018-3
pp.15-20
SANE 2018-05-14
13:15
Tokyo Kikai-Shinko-Kaikan Bldg. Closely Spaced Multiple Target Tracking with Super-Resolution DoA Estimation using Multiple Hypothesis
Masanari Nakamura, Tetsutaro Yamada, Yasushi Obata, Hiroshi Kameda (Mitsubishi Electric Corp.) SANE2018-6
In this paper, we propose multiple target tracking algorithm for dense targets which move toward the radar. When FBSSP-M... [more] SANE2018-6
pp.29-34
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