Blending Textured Images Using a Non-parametric Multiscale MRF Method

Bernard Tiddeman
University of St Andrews
School of Computer Science
KY16 9SS & St Andrews
United Kingdom



In this paper we describe a new method for improving the representation of textures in blends of multiple images based on a Markov Random Field (MRF) algorithm. We show that direct application of an MRF texture synthesis algorithm across a set of images is unable to capture both the "averageness" of the global image appearance as well as specific textural components. To overcome this problem we vary the width of the Parzen window (used to smooth the conditional probability distribution of the pixel's intensity) as a function of scale, thus making lower pyramid resolutions closer to the Gaussian mean, while maintaining the high resolution textures. We also show that approximating the maxima of the conditional probability distributions with a weighted-average produces very similar results with a significant increase in speed.