Image Restoration Using Generalized Deterministic Annealing

✍ Scribed by Scott T. Acton

• Publisher: Elsevier Science
• Year: 1997
• Tongue: English
• Weight: 479 KB
• Volume: 7
• Category: Article
• ISSN: 1051-2004
• DOI: 10.1006/dspr.1997.0283

✦ Synopsis

Regularization provides an effective method by which to generate high-quality solutions to the image restoration problem. Typically, an energy functional that balances deconvolution (sharpening) and smoothing is used to evaluate the quality of an iteratively generated solution. The success of the regularization approach is strongly tied to the method used to generate solutions of lower energy and, thus, higher quality. The generalized deterministic annealing (GDA) algorithm is developed here specifically for image restoration. Compared to other regularization techniques, GDA yields improvements to the quality of the restored image and in the computational expense of the restoration process. This paper specifies the numerical method, the parameters, and the energy functional used to generate image restoration solutions via GDA. Results are given that contrast the superior performance of GDA in image restoration against the traditional constrained least squares method, the steepest descent method, and the simulated annealing method.