General Adaptive Monte Carlo Bayesian Image Denoising

TitleGeneral Adaptive Monte Carlo Bayesian Image Denoising
Publication TypeThesis
Year of Publication2009
AuthorsZhang, W.
Academic DepartmentDepartment of Systems Design Engineering
UniversityUniversity of Waterloo
CityWaterloo, Ontario, Canada
Thesis TypeM.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.