In this paper, we present a new approach to image restoration based on a flexible constraint framework that encapsulates structural assumptions about the uncorrupted image. Piecewise and local class (PALC) models are defined and utilized to restore images degraded by linear blurring and additive noise. The restoration process is accomplished by iteratively deconvolving the solution image while simultaneously optimizing local image characteristics defined by the PALC models. Solution images to this ill-posed, combinatorial problem are computed using the novel generalized deterministic annealing (GDA) optimization technique. The results demonstrate high quality image restoration as measured by local feature integrity, improvement in signal-to-noise ratio, and reduction of restoration artifacts, especially in the presence of heavy-tailed additive noise. >
['Scott T. Acton', 'Alan C. Bovik']
Piecewise and local class models for image restoration