UWSpace is currently experiencing technical difficulties resulting from its recent migration to a new version of its software. These technical issues are not affecting the submission and browse features of the site. UWaterloo community members may continue submitting items to UWSpace. We apologize for the inconvenience, and are actively working to resolve these technical issues.
 

General Adaptive Monte Carlo Bayesian Image Denoising

Thumbnail Image

Files

Zhang_Wen.pdf (8.81 MB)

Date

2010-01-13T17:58:22Z

Authors

Zhang, Wen

Journal Title

Journal ISSN

Volume Title

Publisher

University of Waterloo

Abstract

Image noise reduction, or denoising, is an active area of research, although many of the techniques cited in the literature mainly target additive white noise. With an emphasis on signal-dependent noise, this thesis presents the General Adaptive Monte Carlo Bayesian Image Denoising (GAMBID) algorithm, a model-free approach based on random sampling. Testing is conducted on synthetic images with two different signal-dependent noise types as well as on real synthetic aperture radar and ultrasound images. Results show that GAMBID can achieve state-of-the-art performance, but suffers from some limitations in dealing with textures and fine low-contrast features. These aspects can by addressed in future iterations when GAMBID is expanded to become a versatile denoising framework.

Description

Keywords

image processing, noise reduction, Monte Carlo methods, multiplicative noise, Bayesian estimation, speckle

LC Keywords

Citation

URI

http://hdl.handle.net/10012/4920

Collections

Theses
Systems Design Engineering