Paper Abstract and Keywords |
Presentation |
2021-12-03 15:05
Optimizing Block Size for Low-Level Gaussian Noise Estimation Takashi Suzuki (Micro-Technica), Hiroyuki Tsuji, Tomoaki Kimura (KAIT) SIS2021-28 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
The estimation method based on MAD (Median Absolute Deviation) exists as a method for estimating the standard deviation σ of Gaussian noise in the image. This estimation method divides the image into 16×16 blocks and calculates the standard deviation for each block. Then, the standard deviation of the Gaussian noise superimposed on the image is estimated using the blocks that are considered to be flat areas and the block size is a fixed size of 16×16. However, in the image with many edges and detailed signals, the fixed size of 16×16 may reduce the number of blocks that are considered as flat areas. As a result, the accuracy of the standard deviation of the estimated Gaussian noise will be reduced. In this paper, we investigate the optimal block size for low-level Gaussian noise depending on the amount of edge and detail signals in the image. The proposed method divides the image into several block sizes and estimates the standard deviation of Gaussian noise by MAD-based noise estimation method for each of them. The standard deviation of the Gaussian noise is then estimated by applying the coefficients from the fuzzy set, controlled by the edge and detail signal content of the image, to the standard deviation for each block size. In this result, the error can be reduced by 5.3% in the low-level Gaussian noise image compared with the conventional method using MAD. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Gaussian Noise / Noise Estimation / Standard Deviation / MAD / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 284, SIS2021-28, pp. 37-42, Dec. 2021. |
Paper # |
SIS2021-28 |
Date of Issue |
2021-11-26 (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) |
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SIS2021-28 |
Conference Information |
Committee |
SIS |
Conference Date |
2021-12-03 - 2021-12-03 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
SIS |
Conference Code |
2021-12-SIS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Optimizing Block Size for Low-Level Gaussian Noise Estimation |
Sub Title (in English) |
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Keyword(1) |
Gaussian Noise |
Keyword(2) |
Noise Estimation |
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Standard Deviation |
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MAD |
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1st Author's Name |
Takashi Suzuki |
1st Author's Affiliation |
Micro-Technica Co., Ltd. (Micro-Technica) |
2nd Author's Name |
Hiroyuki Tsuji |
2nd Author's Affiliation |
Kanagawa Institute of Technology (KAIT) |
3rd Author's Name |
Tomoaki Kimura |
3rd Author's Affiliation |
Kanagawa Institute of Technology (KAIT) |
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Speaker |
Author-1 |
Date Time |
2021-12-03 15:05:00 |
Presentation Time |
25 minutes |
Registration for |
SIS |
Paper # |
SIS2021-28 |
Volume (vol) |
vol.121 |
Number (no) |
no.284 |
Page |
pp.37-42 |
#Pages |
6 |
Date of Issue |
2021-11-26 (SIS) |
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