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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IA, SITE, IPSJ-IOT [detail] | 2024-03-13 13:40 |
Okinawa | Miyakojima City Future Creation Center (Primary: On-site, Secondary: Online) |
A Steganalysis of Image Steganography using Real Image Denoising Shinnosuke Toguchi, Takamichi Miyata (CIT)  SITE2023-100 IA2023-106 |
Image steganography is a technique for embedding secret messages in images. SteganoGAN, one of the previous methods, use... [more] | SITE2023-100 IA2023-106 pp.195-202 |
| MI | 2024-03-04 09:24 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
MI2023-64 |
(To be available after the conference date) [more] | MI2023-64 pp.103-105 |
| ITS, IE, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2024-02-20 12:45 |
Hokkaido | Hokkaido Univ. | 3D CG Coded Image Noise Removal and Quality Assessment Based on Optimal Design of Total Variation Regularization Norifumi Kawabata (Kanazawa Gakuin Univ.) ITS2023-67 IE2023-56 |
Sparse coding techniques, which reproduce and represent images with as few combinations as possible from a small amount ... [more] | ITS2023-67 IE2023-56 pp.112-117 |
| SIP, IT, RCS | 2024-01-19 13:30 |
Miyagi | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Problem of Adversarial Attacks on CNN-based Image Classifiers and Countermeasures Minoru Kuribayashi (Tohoku Univ.) IT2023-67 SIP2023-100 RCS2023-242 |
It is well-known that discriminative models based on deep learning techniques may cause misclassification if adversarial... [more] | IT2023-67 SIP2023-100 RCS2023-242 p.204 |
| PRMU, IPSJ-CVIM, IPSJ-DCC, IPSJ-CGVI | 2023-11-17 14:40 |
Tottori | (Primary: On-site, Secondary: Online) |
Diffusion-based Geometric Unwarping and Illumination Correction for Document Images Sota Imahayashi, Guoqing Hao, Satoshi Iizuka, Kazuhiro Fukui (Univ. of Tsukuba) PRMU2023-36 |
This study proposes a method to improve the visibility of document images by correcting distortions and re-illuminating ... [more] | PRMU2023-36 pp.113-118 |
| SIS | 2023-03-02 14:40 |
Chiba | Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
QR code image dnoising netwroks based on decodability assessment Kazumitsu Takahashi, Makoto Nakashizuka (CIT) SIS2022-46 |
In this paper, an image denoising method for QR code images is proposed. The image recovery from the degraded QR code im... [more] | SIS2022-46 pp.33-36 |
| SIS | 2022-12-06 10:30 |
Osaka | (Primary: On-site, Secondary: Online) |
Non Local Means Based on Local Statistics for Image Sequence Denoising Ayae Takei, Mitsuhiko Meguro (Nihon Univ.) SIS2022-37 |
In this paper, we propose an extended Non Local Means for denoising noisy image sequences.Non Local Means is an image de... [more] | SIS2022-37 pp.80-85 |
| SIP | 2022-08-25 14:33 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Structured Deep Image Prior with Interscale Thresholding Jikai Li, Shogo Muramatsu (Niigata Univ.) SIP2022-55 |
This work proposes a novel image denoising technique inspired by the deep image prior (DIP) method. Our contribution is ... [more] | SIP2022-55 pp.31-36 |
| CCS, NLP | 2022-06-09 13:25 |
Osaka | (Primary: On-site, Secondary: Online) |
Learning Method for Image Denoising by Weighted Sum of Perceptual Quality Assessment Methods Takamichi Miyata (Chiba Inst. Tech.) NLP2022-2 CCS2022-2 |
Existing deep learning-based denoising methods employ mean squared error (MSE) as a loss function. As a result, the outp... [more] | NLP2022-2 CCS2022-2 pp.7-12 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-19 14:50 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
Low-dose CT Denoising Using Deep CNN Yuta Sadamatsu (KIT), Seiichi Murakami (JGU), Tohru Kamiya (KIT), Li Guangxu (TPU) SIP2022-12 BioX2022-12 IE2022-12 MI2022-12 |
(To be available after the conference date) [more] | SIP2022-12 BioX2022-12 IE2022-12 MI2022-12 pp.67-70 |
| RCS, SR, SRW (Joint) |
2022-03-04 16:40 |
Online | Online | Denoising Method Using Deep Image Prior for Improving Accuracy of Radar Target Detection Koji Endo, Kohei Yamamoto, Tomoaki Ohtsuki (Keio Univ.) RCS2021-299 |
A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide various application... [more] | RCS2021-299 pp.241-246 |
| EA, SIP, SP, IPSJ-SLP [detail] | 2022-03-01 09:20 |
Okinawa | (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Hyperspectral Image Denoising by Graph Spatio-Spectral Total Variation Minimization Shingo Takemoto, Kazuki Naganuma, Shunsuke Ono (Tokyo Tech) EA2021-70 SIP2021-97 SP2021-55 |
We propose a novel denoising method for hyperspectral images (HSI) based on the Graph Spatio-Spectral Total Variation (G... [more] | EA2021-70 SIP2021-97 SP2021-55 pp.38-43 |
| IPSJ-AVM, CS, IE, ITE-BCT [detail] | 2021-11-25 11:15 |
Online | Online | CS2021-62 IE2021-21 |
A light field (LF) image is composed of multi-view images acquired by slightly offset viewpoints. We propose a novel met... [more] | CS2021-62 IE2021-21 pp.13-18 |
| EMM, IT | 2021-05-21 13:10 |
Online | Online | A Study of Detecting Adversarial Examples Using Sensitivities to Multiple Auto Encoders Yuma Yamasaki, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.), Huy Hong Nguyen, Isao Echizen (NII) IT2021-11 EMM2021-11 |
By removing the small perturbations involved in adversarial examples, the image classification result returns to the cor... [more] | IT2021-11 EMM2021-11 pp.60-65 |
| ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2020-02-27 13:30 |
Hokkaido | Hokkaido Univ. (Cancelled but technical report was issued) |
Chromatic Aberration Correction of Color Images Using Deep Learning with Each Channel Training Based on Contrast Enhancement Naoto Nagashima, Mitsuhiko Meguro (Nihon Univ.) ITS2019-35 IE2019-73 |
In this paper, we propose a new correcting method of chromatic aberration occurring in color images using Deep Learning.... [more] | ITS2019-35 IE2019-73 pp.183-188 |
| IE, CS, IPSJ-AVM, ITE-BCT [detail] | 2019-12-05 11:40 |
Iwate | Aiina Center | [Special Talk]
Representation of moving-image's sparsity and its applications to adaptive moving-image restoration Takahiro Saito (Kanagawa Univ.) CS2019-75 IE2019-55 |
This talk states that statistical sparsity of a moving-image sequence can be properly represented in the domain of the 3... [more] | CS2019-75 IE2019-55 pp.29-34 |
| ITE-BCT, SIS | 2019-10-24 14:50 |
Fukui | Fukui International Activities Plaza | Data-Dependent Non-Local Means based on local variance Junya Matsuoka, Mitsuhiko Meguro (Nihon Univ.) SIS2019-15 |
In this paper, we propose a new correcting Non Local Means (NL-means) algorithms with data-dependent method. The NL-mean... [more] | SIS2019-15 pp.35-40 |
| AI | 2019-09-13 15:50 |
Kagoshima | AI2019-24 |
SSTs are essential information for ocean-related industries but are hard to measure. Although multi-spectral imaging sen... [more] | AI2019-24 pp.31-36 |
|
| IMQ, IE, MVE, CQ (Joint) [detail] |
2019-03-15 13:00 |
Kagoshima | Kagoshima University | Non-blind image deblurring using deep image prior Takanori Fujisawa, Masaaki Ikehara (Keio Univ.) IMQ2018-66 IE2018-150 MVE2018-97 |
Deep learning has become a major tools for image generation and image restoration. General approach for deep learning is... [more] | IMQ2018-66 IE2018-150 MVE2018-97 pp.239-244 |
| 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 |
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