Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/15150
Title: Deriving filter parameters using dual-images for image de-noising
Contributor(s): Wang, Lingyu (author); Leedham, Graham  (author); Cho, Siu-Yeung (author)
Publication Date: 2007
DOI: 10.1109/ISPACS.2007.4445861
Handle Link: https://hdl.handle.net/1959.11/15150
Abstract: 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 sigman², and the gamma value gamma of the specific imaging technique. This paper proposes a technique to automatically derive the signal variance sigmaf² and use this parameter to construct the 'Local Linear Minimum Mean Square Error' (LLMMSE) filter without the need to know the values of sigman² and gamma. 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.
Publication Type: Conference Publication
Conference Details: ISPACS 2007: International Symposium on Intelligent Signal Processing and Communication Systems, Xiamen, China, 28th November - 1st December, 2007
Source of Publication: Proceedings of the International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2007), p. 212-215
Publisher: IEEE: Institute of Electrical and Electronics Engineers
Place of Publication: United States of America
Field of Research (FOR): 080199 Artificial Intelligence and Image Processing not elsewhere classified
080106 Image Processing
080104 Computer Vision
Socio-Economic Objective (SEO): 810107 National Security
810199 Defence not elsewhere classified
890299 Computer Software and Services not elsewhere classified
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Statistics to Oct 2018: Visitors: 295
Views: 408
Downloads: 0
Appears in Collections:Conference Publication

Files in This Item:
2 files
File Description SizeFormat 
Show full item record

Page view(s)

36
checked on May 3, 2019
Google Media

Google ScholarTM

Check

Altmetric


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.