Markov random fields (MRFs) have found widespread use as models of natural image and scene statistics. Despite progress in modeling image properties beyond gradient statistics with high-order cliques, and learning image models from example data, existing MRFs only exhibit a limited ability of actually capturing natural image statistics.
In this talk I will present recent work that investigates this limitation of previous filter-based MRF models, including Fields of Experts (FoEs). We found that these limitations are due to inadequacies in the leaning procedure and suggest various modifications to address them. These "secrets of FoE learning" allow training more suitable potential functions, whose shape approaches that of a Dirac-delta function, as well as models with larger and more filters.
Our experiments not only indicate a substantial improvement of the models' ability to capture relevant statistical properties of natural images, but also demonstrate a significant performance increase in a denoising application to levels previously unattained by generative approaches. This is joint work with Qi Gao.