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Algorithme Hybride de Débruitage Image/Vidéo à base de Réseaux de Neurones Convolutionnels (CNN).

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dc.contributor.author TERAI Salim.
dc.date.accessioned 2021-11-03T12:29:26Z
dc.date.available 2021-11-03T12:29:26Z
dc.date.issued 2021-09-14
dc.identifier.uri https://dspace.univ-bba.dz:443/xmlui/handle/123456789/1062
dc.description.abstract Nowadays, Machine Learning has proven itself as a contender for modern researches on the technical domain. We propose in this manuscript to exploit a certain field from machine learning, which is the deep learning. With the Deep Learning we’ll do a study about image and Video denoising through deep neural network especially CNN (Convolutional Neural Network) according to several recent algorithms namely: DVDnet, FastDVDnet, FFDnet, DnCNN. We shall compare them with classical Image/Video denoising algorithms: BM3D, VBM4D, VNLB, SPTWO. By this comparative study, we will use an evaluation criterion by calculating PSNR, SSIM, MSE, and RMSE in addition to running time and visual quality of the obtained denoised images/videos. en_US
dc.language.iso fr en_US
dc.publisher Faculté des Sciences et Technologies en_US
dc.relation.ispartofseries ;EL/M/2021/19
dc.subject Deep Learning, Denoising, Deep neural network, CNN, PSNR, SSIM en_US
dc.title Algorithme Hybride de Débruitage Image/Vidéo à base de Réseaux de Neurones Convolutionnels (CNN). en_US
dc.type Thesis en_US


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