General Adaptive Monte Carlo Bayesian Image Denoising
| dc.contributor.author | Zhang, Wen | |
| dc.date.accessioned | 2010-01-13T17:58:22Z | |
| dc.date.available | 2010-01-13T17:58:22Z | |
| dc.date.issued | 2010-01-13T17:58:22Z | |
| dc.date.submitted | 2010 | |
| dc.description.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. | en |
| dc.identifier.uri | http://hdl.handle.net/10012/4920 | |
| dc.language.iso | en | en |
| dc.pending | false | en |
| dc.publisher | University of Waterloo | en |
| dc.subject | image processing | en |
| dc.subject | noise reduction | en |
| dc.subject | Monte Carlo methods | en |
| dc.subject | multiplicative noise | en |
| dc.subject | Bayesian estimation | en |
| dc.subject | speckle | en |
| dc.subject.program | System Design Engineering | en |
| dc.title | General Adaptive Monte Carlo Bayesian Image Denoising | en |
| dc.type | Master Thesis | en |
| uws-etd.degree | Master of Applied Science | en |
| uws-etd.degree.department | Systems Design Engineering | en |
| uws.peerReviewStatus | Unreviewed | en |
| uws.scholarLevel | Graduate | en |
| uws.typeOfResource | Text | en |