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Summary: 652 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 8, NO. 5, MAY 1999
Piecewise and Local Image Models for Regularized
Image Restoration Using Cross-Validation
Scott T. Acton, Senior Member, IEEE, and Alan Conrad Bovik, Fellow, IEEE
Abstract--We describe two broad classes of useful and physi-
cally meaningful image models that can be used to construct novel
smoothing constraints for use in the regularized image restoration
problem. The two classes, termed piecewise image models (PIM's)
and local image models (LIM's), respectively, capture unique
image properties that can be adapted to the image and that reflect
structurally significant surface characteristics. Members of the
PIM and LIM classes are easily formed into regularization oper-
ators that replace differential-type constraints. We also develop
an adaptive strategy for selecting the best PIM or LIM for a given
problem (from among the defined class), and we explain the con-
struction of the corresponding regularization operators. Consid-
erable attention is also given to determining the regularization pa-
rameter via a cross-validation technique, and also to the selection
of an optimization strategy for solving the problem. Several re-
sults are provided that illustrate the processes of model selection,
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