IEICE Technical Committee Submission System
Conference Schedule
Online Proceedings
[Sign in]
Tech. Rep. Archives
    [Japanese] / [English] 
( Committee/Place/Topics  ) --Press->
 
( Paper Keywords:  /  Column:Title Auth. Affi. Abst. Keyword ) --Press->

All Technical Committee Conferences  (Searched in: All Years)

Search Results: Conference Papers
 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 60  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
IE, MVE, CQ, IMQ
(Joint) [detail]
2024-03-14
11:00
Okinawa Okinawa Sangyo Shien Center
(Primary: On-site, Secondary: Online)
[Invited Talk] From Pixels to Precision: Passing into the Future of Super-Resolution Mastery
Supatta Viriyavisuthisakul (PIM) IMQ2023-42 IE2023-97 MVE2023-71
Single Image Super-Resolution (SISR) involves reconstructing low-resolution images to enhance perceptual quality. Recent... [more] IMQ2023-42 IE2023-97 MVE2023-71
p.165
EMM, ITE-ME, IE, LOIS, IEE-CMN, IPSJ-AVM [detail] 2023-09-07
16:30
Osaka Osaka Metropolitan University - Nakamozu Campus- (Hybrid)
(Primary: On-site, Secondary: Online)
Image Restoration using Super-Resolution and Deblur
Akari Dakeno, Terumasa Aoki (TUT) LOIS2023-9 IE2023-19 EMM2023-56
With the development of deep learning, many studies on Super-Resolution (SR) using CNN have widely been done in the worl... [more] LOIS2023-9 IE2023-19 EMM2023-56
pp.12-17
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
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
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
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]
IE, CS, IPSJ-AVM, ITE-BCT [detail] 2019-12-06
14:15
Iwate Aiina Center Image Restoration using a Codec
Yoko Sogabe, Shiori Sugimoto, Takayuki Kurozumi, Hideaki Kimata (NTT) CS2019-86 IE2019-66
Image restoration, which is an inverse problem such as compressed
sensing and super-resolution, requires priors (e.g. ... [more]
CS2019-86 IE2019-66
pp.105-109
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
NC, MBE
(Joint)
2019-03-06
16:15
Tokyo University of Electro Communications Examination of Super Resolution and Noise Removal for MicroCT Image
Miku Mashimo, Hayaru Shouno (UEC) NC2018-86
The purpose of this research is to increase the resolution of MicroCT (Computed Tomography) images.
The MicroCT image i... [more]
NC2018-86
pp.227-232
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
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
SIP, EA, SP, MI
(Joint) [detail]
2018-03-19
09:35
Okinawa   Super-resolution of MRI images using SRCNN and its Evaluation
Hikaru Niida, Kiminori Matsuzaki (Kochi Univ. of Tech.) MI2017-63
Super-resolution using deep convolutional neural networks (SRCNN) was proposed by Dong et al. in 2014. SRCNN has also be... [more] MI2017-63
pp.1-4
SIP 2016-08-26
10:35
Chiba Chiba Institute of Technology, Tsudanuma Campus Super resolution of vein images using convolutional neural networks
Koji Kashihara (Tokushima Univ.) SIP2016-80
If super-resolution techniques improve the quality of near-infrared images with a low signal-to-noise ratio, they could ... [more] SIP2016-80
pp.39-44
RECONF 2016-05-19
13:00
Kanagawa FUJITSU LAB. FPGA Implementation of a Super-Resolution System
Taito Manabe, Yuichiro Shibata, Kiyoshi Oguri (Nagasaki Univ.) RECONF2016-5
In this study, we implement a real-time super-resolution system for moving images using a convolutional neural network o... [more] RECONF2016-5
pp.17-22
IE, IMQ, MVE, CQ
(Joint) [detail]
2016-03-07
18:25
Okinawa   Image Reduction Based on waifu2x
Kohei Inoue, Kenji Hara, Kiichi Urahama (Kyushu Univ.) IMQ2015-52 IE2015-151 MVE2015-79
Recently, a high-quality image enlargement tool called waifu2x has been released and become the topic on the Internet. I... [more] IMQ2015-52 IE2015-151 MVE2015-79
pp.135-138
 Results 1 - 20 of 60  /  [Next]  
Choose a download format for default settings. [NEW !!]
Text format pLaTeX format CSV format BibTeX format
Copyright and reproduction : All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan