Nonlinear image estimation using piecewise and local image models

—We introduce a new approach to image estimation based on a flexible constraint framework that encapsulates meaningful structural image assumptions. Piecewise image models (PIM’s) and local image models (LIM’s) are defined and utilized 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 ...

• Type: Journal
• Year: 1998
• Where: TIP
• Authors: Scott T. Acton, Alan C. Bovik
• Keywords: Image, Image Estimation, Local Image, TIP 1998