Learning visual operators from examples: a new paradigm in image processing

This paper presents a general strategy for designing efficient visual operators. The approach is highly task-oriented and what constitutes the relevant information is defined by a set of examples. The examples are pairs of images displaying a strong dependence in the chosen feature but are otherwise independent. Particularly important concepts in the work are mutual information and canonical correlation. Visual operators learned from examples are presented, e.g., local shift-invariant orientation operators and image content-invariant disparity operators. Interesting similarities to biological vision functions are observed.