|Title||General Adaptive Monte Carlo Bayesian Image Denoising|
|Year of Publication||2009|
|Academic Department||Department of Systems Design Engineering|
|University||University of Waterloo|
|City||Waterloo, Ontario, Canada|
|Thesis Type||M.A.Sc Thesis|
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.