Learning structural and corruption information from samples for Markov-random-field edge detection enhancement

摘要

We have advanced Markov random field research by addressing the issue of obtaining a reasonable, non-trivial, noise model. We have introduced the concept of a double neighborhood MRF. In the past we have estimated MRF probabilities by sampling neighborhood frequencies from images. Now we address the issue of noise models by sampling from pairs of original images together with noisy imagery. Thus we create a probability density function for pairs of neighborhoods across both images. This models the noise within the MRF probability density function without having to make assumptions about its form. This provides an easy way to generate Markov random fields for annealing or other relaxation methods. We have successfully applied this technique, combined with a technique of Hancock and Kittler which adds theoretical noise to an MRF density function, to the problem binary image reconstruction. We now apply it to edge detection enhancement of artificial images. We train the double neighborhood MRF on true edge-maps and edge-maps generated as output of a Sobel edge detector. Our method improves the generated edge-maps - visually, and using the metrics of number of bits incorrect, and Pratt's figure of merit for edge detectors. We have also successfully improved the output edge-maps of some real images.