  Visual illusions may be explained by the likelihood of patches in real-world
images, as argued by input-driven paradigms in Neuro-Science. However, neither
the data nor the tools existed in the past to extensively support these
explanations. The era of big data opens a new opportunity to study input-driven
approaches. We introduce a tool that computes the likelihood of patches, given
a large dataset to learn from. Given this tool, we present a model that
supports the approach and explains lightness and color visual illusions in a
unified manner. Furthermore, our model generates visual illusions in natural
images, by applying the same tool, reversely.
