Deep convolutional neural network for mixed random impulse and Gaussian noise reduction in digital images

Deep convolutional neural network for mixed random impulse and Gaussian noise reduction in digital images

This study utilises a deep convolutional neural network (CNN) implementing regularisation and batch normalisation for the removal of mixed, random, impulse, and Gaussian noise of various levels from digital images. This deep CNN achieves minimal loss of detail and yet yields an optimal estimation of...

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Bibliographic Details
Main Authors: Mehdi Mafi, Walter Izquierdo, Harold Martin, Mercedes Cabrerizo, Malek Adjouadi
Format: Article
Language:English
Published: Wiley 2020-12-01
Series:IET Image Processing
Subjects:
digital images
deep CNN
minimal loss
optimal estimation
known noise mixtures
unknown noise mixtures
Online Access:https://doi.org/10.1049/iet-ipr.2019.0931

Internet

https://doi.org/10.1049/iet-ipr.2019.0931

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