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 |