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| Presentation | 2024-03-15 10:40 A study of low-level Gaussian noise estimating by using machine learning Takashi Suzuki (MicroTechnica), Tomoaki Kimura (Kanagawa institute of technology)  SIS2023-57 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this study, we investigate a noise estimation method for low-level Gaussian noise with a standard deviation of less than 10. Although various methods for estimating Gaussian noise have been proposed in the past, noise estimation of low-level Gaussian noise has large errors in images with many edge and detail signals. In this paper, we consider applying the epsilon filter to the noise estimation method in order to consider the effects of edge and detail signals, and then changing the value of epsilon in order to consider the relationship between noise level and edge and detail signals. This relationship is then estimated using an approximation function, and machine learning is used to determine the relationship between noise level and edge/detail signal by changing the epsilon value. We then estimate this relationship using an approximation function and investigate whether it is possible to obtain the standard deviation of low-level Gaussian noise superimposed on the final image by using machine learning based on the situation of the approximation function for various images. It is confirmed that estimated noise value obtained by the proposed method is better than that by conventional methods. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Low-level Gaussian noise / noise estimation / machine learning / Excel solver / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 440, SIS2023-57, pp. 67-72, March 2024. | |
| Paper # | SIS2023-57 | |
| Date of Issue | 2024-03-07 (SIS) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| 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) | |
| Download PDF | SIS2023-57 |
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| Conference Information | |
| Committee | SIS |
| Conference Date | 2024-03-14 - 2024-03-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kanagawa Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Soft computing, etc. |
| Paper Information | |
| Registration To | SIS |
| Conference Code | 2024-03-SIS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A study of low-level Gaussian noise estimating by using machine learning |
| Sub Title (in English) | |
| Keyword(1) | Low-level Gaussian noise |
| Keyword(2) | noise estimation |
| Keyword(3) | machine learning |
| Keyword(4) | Excel solver |
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| 1st Author's Name | Takashi Suzuki |
| 1st Author's Affiliation | MicroTechnica Co., Ltd. (MicroTechnica) |
| 2nd Author's Name | Tomoaki Kimura |
| 2nd Author's Affiliation | Kanagawa institute of technology (Kanagawa institute of technology) |
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| Speaker | Author-2 |
| Date Time | 2024-03-15 10:40:00 |
| Presentation Time | 20 minutes |
| Registration for | SIS |
| Paper # | SIS2023-57 |
| Volume (vol) | vol.123 |
| Number (no) | no.440 |
| Page | pp.67-72 |
| #Pages | 6 |
| Date of Issue | 2024-03-07 (SIS) |
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