TY - GEN
T1 - Rapid and Accurate Local Gaussian Noise Removal
AU - Seta, Shogo
AU - Nakahara, Yusuke
AU - Yamaguchi, Takuro
AU - Ikehara, Masaaki
N1 - Publisher Copyright:
© 2020 APSIPA.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12/7
Y1 - 2020/12/7
N2 - In this paper, we propose a rapid and high-accuracy Gaussian noise removal method by applying the learning linear filter used in RAISR for super-resolution. Our algorithm is a rapid local method, yet produces comparable results to the accuracy of the non-local method known for its high accuracy. The novelty of this paper is that the same processing as super-resolution is incorporated into denoising. The conventional local processing includes smoothing processing, and has a problem that high-frequency components of an original signal are lost while reducing the noise. In order to solve the problem, this method incorporates a super-resolution method that compensates for high-frequency components as post-processing. The super-resolution method utilizes a process that applies a learning linear filter according to the feature of patches in RAISR. Because the proposed method consists of local precessing, its operation is rapid compared to non local processing like BM3D.
AB - In this paper, we propose a rapid and high-accuracy Gaussian noise removal method by applying the learning linear filter used in RAISR for super-resolution. Our algorithm is a rapid local method, yet produces comparable results to the accuracy of the non-local method known for its high accuracy. The novelty of this paper is that the same processing as super-resolution is incorporated into denoising. The conventional local processing includes smoothing processing, and has a problem that high-frequency components of an original signal are lost while reducing the noise. In order to solve the problem, this method incorporates a super-resolution method that compensates for high-frequency components as post-processing. The super-resolution method utilizes a process that applies a learning linear filter according to the feature of patches in RAISR. Because the proposed method consists of local precessing, its operation is rapid compared to non local processing like BM3D.
KW - denoising
KW - gaussian noise
KW - joint bilateral filter
KW - RAISR
KW - super-resolution
UR - http://www.scopus.com/inward/record.url?scp=85100924642&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100924642&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85100924642
T3 - 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
SP - 1222
EP - 1225
BT - 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020
Y2 - 7 December 2020 through 10 December 2020
ER -