TY - GEN
T1 - Deriving filter parameters using dual-images for image de-noising
AU - Wang, Lingyu
AU - Leedham, Graham
AU - Cho, Siu Yeung
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - This paper presents a novel technique to derive the filter parameters for removing signal dependent noise (SDN) in the image. In order to remove SDN, many de-noising algorithms rely on a priori knowledge of noise parameters, especially the variance σn2, and the gamma value γ of the specific imaging technique. This paper proposes a technique to automatically derive the signal variance σf2 and use this parameter to construct the Local Linear Minimum Mean Square Error (LLMMSE) filter without the need to know the values of σn2 and γ. Two image instances of the same noisy scene are used to calculate the signal variance which is then used to construct the LLMMSE filter. Experiments with both the "Lena" image and real-life far-infrared (FIR) vein pattern images showed that the proposed technique can predict the signal variance consistently, and the constructed LLMMSE filter performs well in removing the signal dependent noise.
AB - This paper presents a novel technique to derive the filter parameters for removing signal dependent noise (SDN) in the image. In order to remove SDN, many de-noising algorithms rely on a priori knowledge of noise parameters, especially the variance σn2, and the gamma value γ of the specific imaging technique. This paper proposes a technique to automatically derive the signal variance σf2 and use this parameter to construct the Local Linear Minimum Mean Square Error (LLMMSE) filter without the need to know the values of σn2 and γ. Two image instances of the same noisy scene are used to calculate the signal variance which is then used to construct the LLMMSE filter. Experiments with both the "Lena" image and real-life far-infrared (FIR) vein pattern images showed that the proposed technique can predict the signal variance consistently, and the constructed LLMMSE filter performs well in removing the signal dependent noise.
KW - Noise removal
KW - Parameter estimation
KW - Signal dependent noise
UR - http://www.scopus.com/inward/record.url?scp=43749103772&partnerID=8YFLogxK
U2 - 10.1109/ISPACS.2007.4445861
DO - 10.1109/ISPACS.2007.4445861
M3 - Conference contribution
AN - SCOPUS:43749103772
SN - 9781424414475
T3 - 2007 International Symposium on Intelligent Signal Processing and Communications Systems, ISPACS 2007 - Proceedings
SP - 212
EP - 215
BT - 2007 International Symposium on Intelligent Signal Processing and Communications Systems, ISPACS 2007 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2007 International Symposium on Intelligent Signal Processing and Communications Systems, ISPACS 2007
Y2 - 28 November 2007 through 1 December 2007
ER -