We study the complementary behaviors of external and internal examples in
image restoration, and are motivated to formulate a composite dictionary design
framework. The composite dictionary consists of the global part learned from
external examples, and the sample-specific part learned from internal examples.
The dictionary atoms in both parts are further adaptively weighted to emphasize
their model statistics. Experiments demonstrate that the joint utilization of
external and internal examples leads to substantial improvements, with
successful applications in image denoising and super resolution.