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IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 7, NO. 7, JULY 1998 979 Nonlinear Image Estimation Using
 

Summary: IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 7, NO. 7, JULY 1998 979
Nonlinear Image Estimation Using
Piecewise and Local Image Models
Scott T. Acton, Member, IEEE, and Alan C. Bovik, Fellow, IEEE
Abstract--We introduce a new approach to image estimation
based on a flexible constraint framework that encapsulates mean-
ingful structural image assumptions. Piecewise image models
(PIM's) and local image models (LIM's) are defined and uti-
lized to estimate noise-corrupted images. PIM's and LIM's are
defined by image sets obeying certain piecewise or local image
properties, such as piecewise linearity, or local monotonicity. By
optimizing local image characteristics imposed by the models,
image estimates are produced with respect to the characteristic
sets defined by the models. Thus, we propose a new general
formulation for nonlinear set-theoretic image estimation. Detailed
image estimation algorithms and examples are given using two
PIM's: piecewise constant (PICO) and piecewise linear (PILI)
models, and two LIM's: locally monotonic (LOMO) and locally
convex/concave (LOCO) models. These models define properties
that hold over local image neighborhoods, and the corresponding

  

Source: Acton, Scott - Department of Electrical and Computer Engineering, University of Virginia

 

Collections: Computer Technologies and Information Sciences