Analyzing images, to estimate the underlying parameters that lead to their formation is fundamentally an inverse problem. Since the observed image alone is usually not enough to uniquely determine these parameters, statistical models are frequently used to choose a likely solution from amongst those that are consistent with this observation. In this dissertation, we use such a statistical approach to develop image models and corresponding inference algorithms for two vision applications and then explore image statistics in a new domain.