The robustness of proximal penalty algorithms in restoration of noisy image

Authors : Sabrina GHERAİBİA, Amar GUESMİA, Noureddine DAİLİ

Pages : 1043-1052

View : 56 | Download : 9

Publication Date : 2017-12-01

Article Type : Research Paper

Abstract :The nondifferentiable convex optimization has an importance crucial in the image restoration for this and in this article we present the performance of the Prox method adapted to the restoration of noisy images. Following of our article insert ignore into journalissuearticles values([12]);, we illustrate in this work thesuperior efficacy of this algorithm “Prox” insert ignore into journalissuearticles values([12]); then we are comparing the obtained numerical results with the algorithms of Wiener filtering insert ignore into journalissuearticles values([7], [16]);, total variation insert ignore into journalissuearticles values([5]); and wavelet soft-thresholding denoising insert ignore into journalissuearticles values([1], [12], [13]);, in terms of image quality and convergence. Our first experiments showed that by applying of Prox algorithm for restoration of noised image by the white Gaussian noise we obtain a top results of denosed image with high quality insert ignore into journalissuearticles values(net, not rehearsed and unsmoothed; textures are preserved); in addition to the convergence of the algorithm is ensured whatever the values of SNR.
Keywords : proximal penalty algorithms, image restoration, SNR, convergence

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