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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-03-01 16:35 |
Okinawa | (Primary: On-site, Secondary: Online) |
Large Scale Pre-training and Dynamic Convolution for Image Restoration Under Bad Weather Conditions Shugo Yamashita, Masaaki Ikehara (Keio Univ.)  EA2023-128 SIP2023-175 SP2023-110 |
We propose a convolution-based Encoder-Decoder model for removing degradation caused by multiple weather conditions such... [more] | EA2023-128 SIP2023-175 SP2023-110 pp.394-399 |
| 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 |
| EA, ASJ-H, ASJ-MA, ASJ-SP | 2023-07-02 15:10 |
Hokkaido | Speech Restoration of Spectrogram Images Printed in a Document "Visible Speech" Published in 1947 Naofumi Aoki (Hokkaido Univ.) EA2023-6 |
The restoration of speech materials recorded in the past might be regarded as a study in acoustical archeology. It may p... [more] | EA2023-6 pp.12-15 |
|
| PRMU, IPSJ-CVIM | 2023-05-18 13:30 |
Aichi | (Primary: On-site, Secondary: Online) |
Image Harmonization Using Diffusion Model for Perceptual Quality Improvement Taito Naruki, Norimichi Ukita (TTI) PRMU2023-1 |
Image harmonization is the task of eliminating the discomfort of color tones that occurs when images are composited. How... [more] | PRMU2023-1 pp.1-5 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-02-28 10:40 |
Okinawa | (Primary: On-site, Secondary: Online) |
Image reconstruction with a diffusion model for robust image classification against unknown degradation Teruaki Akazawa (Tokyo Metro. Univ.), Yuma Kinoshita (Tokai Univ.), Hitoshi Kiya (Tokyo Metro. Univ.) EA2022-83 SIP2022-127 SP2022-47 |
This paper presents an image reconstruction method with a diffusion model for robust image classification against image ... [more] | EA2022-83 SIP2022-127 SP2022-47 pp.49-54 |
| IE | 2023-02-02 15:30 |
Tokyo | NII (Primary: On-site, Secondary: Online) |
[Invited Talk]
How Can We Compress Signs of DCT Coefficients in Image Coding?
-- A Method Inspired by Phase Retrieval -- Chihiro Tsutake (Nagoya Univ.) IE2022-55 |
Compressing the signs of DCT coefficients is an intractable problem in image coding because of their equiprobable charac... [more] | IE2022-55 p.19 |
| HCS | 2023-01-22 10:15 |
Kyoto | Kyoto Institute of Technology (Primary: On-site, Secondary: Online) |
Hand Image Restoration Using a Diminished Reality Technique for Hand Tracking Domu Kawakami, Kazuaki Tanaka (KIT) HCS2022-65 |
Machine learning techniques have enabled highly accurate posture estimation from camera images. However, if the body is ... [more] | HCS2022-65 pp.60-64 |
| PRMU | 2022-12-15 10:30 |
Toyama | Toyama International Conference Center (Primary: On-site, Secondary: Online) |
Single Image Raindrop Removal Using a Non-local Operator and Feature Maps in the Frequency Domain Shinya Ezumi, Masaaki Ikehara (Keio Univ.) PRMU2022-34 |
High-quality raindrop removal is desired for outdoor image processing systems as well as for acquiring good-looking imag... [more] | PRMU2022-34 pp.13-18 |
| MICT, MI | 2022-11-18 14:00 |
Aichi | Nagoya Institute of Technology | Extrapolation of partial X-ray image for prediction of whole body musculoskeletal structure Weiqi Zhang, Yi Gu, Yoshito Otake, Soufi Mazen (NAIST), Keisuke Uemura (Osaka Univ), Masaki Takao (Ehime Univ), Toshiaki Akashi (Juntendo Univ), Kensaku Mori (Nagoya Univ/NII), Kento Aida (NII), Nobuhiko Sugano (Osaka Univ), Yoshinobu Sato (NAIST) MICT2022-38 MI2022-67 |
Image defects and partial disorders are common problems in medical imaging. Image inpainting and extrapolation are helpf... [more] | MICT2022-38 MI2022-67 pp.24-28 |
| AI | 2022-09-16 15:45 |
Shizuoka | (Primary: On-site, Secondary: Online) |
An image restoration method for lecture videos with projected lecture slides Yuma Ito, Masato Kikuchi, Tadachika Ozono (NIT) AI2022-32 |
The lecturers can use their body movements and gestures in the lectures with slides displayed using a projector in the r... [more] | AI2022-32 pp.79-84 |
| 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 |
| SIP | 2022-08-26 15:51 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Design of Structured Convolutional Dictionary by Manifold Optimization for Image Restoration Soushi Takahashi, Shogo Muramatsu (Niigata Univ.) SIP2022-76 |
This work reports on the effectiveness of a structured convolutional dictionary learning that introduces manifold optimi... [more] | SIP2022-76 pp.134-139 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-19 16:10 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
[Invited Talk]
Image and Video Restoration with Deep Learning Satoshi Iizuka (Univ. of Tsukuba) SIP2022-15 BioX2022-15 IE2022-15 MI2022-15 |
In this talk, I will introduce techniques for restoring black-and-white images and videos with high accuracy using deep ... [more] | SIP2022-15 BioX2022-15 IE2022-15 MI2022-15 p.78 |
| EST | 2022-01-28 11:00 |
Online | Online | Blood Vessel Structure Analysis using a Simulation Model for the Purpose of Polyp Shape Recovery from Endoscopic Images Shusuke Kato, Hiroyasu Usami, Akihiko Okazaki, Yuji Iwahori (Chubu Univ.), Ogasawara Naotaka, Kunio Kasugai (Aichi Medical Univ.) EST2021-83 |
In recent years, the incidence of colorectal cancer in Japan has been on the rise. It is essential to realize a medical ... [more] | EST2021-83 pp.130-135 |
| 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 |
| SIP | 2021-08-23 15:40 |
Online | Online | [Invited Talk (Young Researcher)]
Hyperspectral image restoration based on convex optimization Saori Takeyama (Tokyo Tech) SIP2021-31 |
Hyperspectral (HS) images have high-resolution spectral information including invisible light, so they can visible the i... [more] | SIP2021-31 p.18 |
| HIP, VRSJ | 2021-02-18 14:05 |
Online | Online | Reappearance of faded afterimages Yushan Hu, Hiroyuki Ito (Kyushu Univ.) HIP2020-76 |
In this study, we investigated the reappearance of a faded negative afterimage by psychophysical methods. We tested blin... [more] | HIP2020-76 pp.20-24 |
| SIS, ITE-BCT | 2020-10-01 13:40 |
Online | Online | Image Regularization with Morphological Gradient Priors Using Optimization of Multiple Structuring Elements for Each Pixel Hirotaka Oka, Mistuji Muneyasu, Soh Yoshida (Kansai Univ.), Makoto Nakashizuka (CIT) SIS2020-15 |
In image regularization, a method for restoring an image has been proposed in which a morphological gradient is used as ... [more] | SIS2020-15 pp.29-34 |
| MI | 2020-09-03 13:10 |
Online | Online | [Invited Talk]
Manifold modeling in embedded space for image restoration Tatsuya Yokota (Nitech) MI2020-27 |
In this invited talk, I will discuss convolutional neural networks, which have achieved remarkable results in various im... [more] | MI2020-27 pp.43-44 |
| SIP | 2020-08-28 13:30 |
Online | Online | [Invited Talk]
Image smoothing based on L0 gradient regularization and its applications Ryo Matsuoka (Univ. of Kitakyushu) SIP2020-37 |
This talk outlines research on image processing based on L0 gradient regularization that promotes sparseness in the grad... [more] | SIP2020-37 p.33 |
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