@mastersthesis {312,
	title = {General Adaptive Monte Carlo Bayesian Image Denoising},
	year = {2009},
	school = {University of Waterloo},
	type = {M.A.Sc Thesis},
	address = {Waterloo, Ontario, Canada},
	abstract = {<p>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.</p>
},
	author = {W Zhang}
}
