External Seminar: John Kornak (University of California, San Francisco)

[Statistics and Probability Seminar]

Title: Bayesian image analysis in Fourier space.

Abstract: Bayesian image analysis has played an important role in the field of image processing over the last 30 years, largely due to its structured approach to balancing a priori expectations of image characteristics with a model for the image degradation process (noise, blurring etc.). I will provide background on Bayesian image analysis, and in particular discuss the major role played by Markov random fields as prior distributions. I will subsequently describe my reformulation of the conventional Bayesian image analysis paradigm into Fourier space. Spatially correlated processes in conventional image space can be efficiently modeled as a set of independent processes across Fourier space, leading to easy model specification, and relatively fast and straightforward computation. I will give specific examples of applications in medical imaging, and contrast Bayesian image analysis results in Fourier space with results in conventional image space.