The robustness of proximal penalty algorithms in restoration of noisy image

Year 2017, Volume: 46 Issue: 6, 1043 - 1052, 01.12.2017

Sabrina Gheraibia Amar Guesmia Noureddine Daili

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 ([12]), we illustrate in this work thesuperior efficacy of this algorithm “Prox” ([12]) then we are comparing the obtained numerical results with the algorithms of Wiener filtering ([7], [16]), total variation ([5]) and wavelet soft-thresholding denoising ([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 (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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