Investigation of Compound Gauss-Markov Image Field
This Compound Gauss-Markov image model has been proven helpful in image restoration. In this model, a pixel in the image random field is determined by the surrounding pixels according to a predetermined line field. In this thesis, we restored the noisy image based upon the traditional Compound Gauss-Markov image field without the constraint of the model parameters introduced in the original work. The image is restored in two steps iteratively: restoring the line field by the assumed image field and restoring the image field by the just computed line field.
Two methods are proposed to replace the traditional method in solving for the line field. They are probability method and vector method. In probability method, we break away from the limitation of the energy function Vcl(L) and the mystical system parameters Ckll(m,n) and£mw2. In vector method, the line field appears more reasonable than the original method. The image restored by our methods has a similar visual quality but a better numerical value than the original method.
Advisor:Ben-Shung Chow; Chia-Hsiung Kao; Chung-Nan Lee
School:National Sun Yat-Sen University
School Location:China - Taiwan
Source Type:Master's Thesis
Keywords:compound gauss markov random field image segmentation maximum a posteriori probability deterministic search
Date of Publication:08/05/2002