General Adaptive Monte Carlo Bayesian Image Denoising

image processing;noise reduction;Monte Carlo methods;multiplicative noise;Bayesian estimation;speckle;System Design Engineering

Zhang, Wen

University of Waterloo

关键词: image processing;    noise reduction;    Monte Carlo methods;    multiplicative noise;    Bayesian estimation;    speckle;    System Design Engineering;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/4920/1/Zhang_Wen.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

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.