Images of natural scenes contain a rich variety of visual patterns. To learn and recognize these patterns from natural images, it is necessary to construct statistical models for these patterns. In this review article we describe three statistical principles for modeling image patterns: the sparse coding principle, the minimax entropy principle, and the meaningful alignment principle. We explain these three principles and their relationships in the context of modeling images as compositions of Gabor wavelets. These three principles correspond to three regimes of composition patterns of Gabor wavelets, and these three regimes are connected by changes in scale or resolution.