Algorithme Hybride de Débruitage Image/Vidéo à base de Réseaux de Neurones Convolutionnels (CNN).
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.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.identifier.uri | http://10.10.1.6:4000/handle/123456789/1062 | |
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 |