In this image downscaling paper, the authors apply the Expectation-Maximization algorithm to solve an image downscaling problem. I've never done Bayesian statistics, so I find some parts a bit hard to understand. Here's the key part, the calculation of Gaussian kernels in the expectation stage. I'm trying to understand how does one come up with the formulas f and g, highlighted in red and green, and what associated reading do I need to do to understand them further. Thanks!
