Year: 2012
Type: article
Abstract: The aim of this paper is to implement an optimal neural network model to resolve the problem of colour image restoration which consists of retrieving original image degraded by invariant blur and corrupted by random white additive noise.We propose in this paper an algorithm which implements a general network of reduced neural networks model and adaptive regularization.The developed model is based on the original model of zhou and the modified model of Paik and katsaggelos.The adaptive regularization parameter is used in our case when degradation model contains an additive component (additive noise) in order to obtain a compromise between image sharpness and noise elimination.It is chosen using an iterative algorithm which calculates the best value that maximizes the PSNR of the restored image.Our model presents some improvements in terms of complexity and quality of restored images.It is shown by experiments that restored images obtained by the proposed model are better in terms of both numerical measurement and visual quality.
DOI: 10.5120/8583-2331
Authors: Fillali Ferhat, Maza Sofiane, Graini Abid
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2133051648