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http://hdl.handle.net/10012/4920
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| Title: | General Adaptive Monte Carlo Bayesian Image Denoising |
| Authors: | Zhang, Wen |
| Keywords: | image processing
noise reduction
Monte Carlo methods
multiplicative noise
Bayesian estimation
speckle |
| Approved Date: | 13-Jan-2010 |
| Date Submitted: | 2010 |
| 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. |
| Program: | System Design Engineering |
| Department: | Systems Design Engineering |
| Degree: | Master of Applied Science |
| URI: | http://hdl.handle.net/10012/4920 |
| Appears in Collections: | Faculty of Engineering Theses and Dissertations
Electronic Theses and Dissertations (UW)
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