The outlier process: unifying line processes and robust statistics

This paper unifies “line-process” approaches for regularization with discontinuities and robust estimation techniques. We generalize the notion of a “line process” to that of an analog “outlier process” and show that a problem formulated in terms of outlier processes can be viewed an terms of robust statistics. We also characterize a class of robust statistical problems for which an equivalent outlier-process formulation exists and give a straightforward method for converting a robust estimation problem into an outlier-process formulation. This outlier-processes approach provides a general framework which subsumes the traditional lane-process approaches as well as a wade class of robust estimation problems. Examples in image reconstruction and optical flow are used to illustrate the approach.