Title: Iterative Regularized Image Restoration Based on a Generalized Gaussian Model for Noise
Abstract: Based on the assumption of a generalized Gaussian model for the additive noise, this paper develops the regularized image restoration algorithm and proposes an l p-norm data item to the regularization objective functionnal instead of the usual quadratic data item. Meanwhile, the paper applies an adaptive method for choosing the regularization parameter. An iterative algorithm is utilized for obtaining the restored image and the regularization parameter, which can be determined in terms of the partly restored image at each iteration step therefore allowing for the simultaneous determination of the restoration of the degraded image and the value of the regularization parameter. Numerical experiments demonstrate that our method results in high restoration performance when the image was blurred by a Gaussian PSF and an additive generalized Gaussian noise, especially when the shape parameter p≤1.
Publication Year: 2004
Publication Date: 2004-01-01
Language: en
Type: article
Access and Citation
Cited By Count: 1
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot