抄録
Mixed noise removal is a major problem in image processing. Different noises have different properties and it is required to use an appropriate removal method for each noise. Therefore, removal of mixed noise needs the combination of removal algorithms for each contained noise. We aim at the removal of the mixed noise composed of Additive White Gaussian Noise (AWGN) and Random-Valued Impulse Noise (RVIN). Many conventional methods cannot remove the mixed noise effectively and may lose image details. In this paper, we propose a new mixed noise removal method utilizing Direction Weighted Median filter (DWM filter) and Block Matching and 3D filtering method (BM3D). Although the combination of the DWMfilter for RVIN and BM3D for AWGN removes almost all the mixed noise, it still loses some image details. We find the cause in the miss-detection of the image details as RVIN and solve the problem by re-detection with the difference of an input noisy image and the output by the combination. The re-detection process removes only salient noise which BM3D cannot remove and therefore preserves image details. These processes lead to the high performance removal of the mixed noise while preserving image details. Experimental results show our method obtains denoised images with clearer edges and textures than conventional methods.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 2451-2457 |
| ページ数 | 7 |
| ジャーナル | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |
| 巻 | E100A |
| 発行部数 | 11 |
| DOI | |
| 出版物ステータス | Published - 2017 11 1 |
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ASJC Scopus subject areas
- Signal Processing
- Computer Graphics and Computer-Aided Design
- Electrical and Electronic Engineering
- Applied Mathematics
これを引用
Detail preserving mixed noise removal by DWM filter and BM3D. / Yamaguchi, Takuro; Suzuki, Aiko; Ikehara, Masaaki.
：: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 巻 E100A, 番号 11, 01.11.2017, p. 2451-2457.研究成果: Article
}
TY - JOUR
T1 - Detail preserving mixed noise removal by DWM filter and BM3D
AU - Yamaguchi, Takuro
AU - Suzuki, Aiko
AU - Ikehara, Masaaki
PY - 2017/11/1
Y1 - 2017/11/1
N2 - Mixed noise removal is a major problem in image processing. Different noises have different properties and it is required to use an appropriate removal method for each noise. Therefore, removal of mixed noise needs the combination of removal algorithms for each contained noise. We aim at the removal of the mixed noise composed of Additive White Gaussian Noise (AWGN) and Random-Valued Impulse Noise (RVIN). Many conventional methods cannot remove the mixed noise effectively and may lose image details. In this paper, we propose a new mixed noise removal method utilizing Direction Weighted Median filter (DWM filter) and Block Matching and 3D filtering method (BM3D). Although the combination of the DWMfilter for RVIN and BM3D for AWGN removes almost all the mixed noise, it still loses some image details. We find the cause in the miss-detection of the image details as RVIN and solve the problem by re-detection with the difference of an input noisy image and the output by the combination. The re-detection process removes only salient noise which BM3D cannot remove and therefore preserves image details. These processes lead to the high performance removal of the mixed noise while preserving image details. Experimental results show our method obtains denoised images with clearer edges and textures than conventional methods.
AB - Mixed noise removal is a major problem in image processing. Different noises have different properties and it is required to use an appropriate removal method for each noise. Therefore, removal of mixed noise needs the combination of removal algorithms for each contained noise. We aim at the removal of the mixed noise composed of Additive White Gaussian Noise (AWGN) and Random-Valued Impulse Noise (RVIN). Many conventional methods cannot remove the mixed noise effectively and may lose image details. In this paper, we propose a new mixed noise removal method utilizing Direction Weighted Median filter (DWM filter) and Block Matching and 3D filtering method (BM3D). Although the combination of the DWMfilter for RVIN and BM3D for AWGN removes almost all the mixed noise, it still loses some image details. We find the cause in the miss-detection of the image details as RVIN and solve the problem by re-detection with the difference of an input noisy image and the output by the combination. The re-detection process removes only salient noise which BM3D cannot remove and therefore preserves image details. These processes lead to the high performance removal of the mixed noise while preserving image details. Experimental results show our method obtains denoised images with clearer edges and textures than conventional methods.
KW - BM3D
KW - DWM filter
KW - Image denoising
KW - Mixed noise
UR - http://www.scopus.com/inward/record.url?scp=85033463125&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85033463125&partnerID=8YFLogxK
U2 - 10.1587/transfun.E100.A.2451
DO - 10.1587/transfun.E100.A.2451
M3 - Article
AN - SCOPUS:85033463125
VL - E100A
SP - 2451
EP - 2457
JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
SN - 0916-8508
IS - 11
ER -