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

Mafi, Mehdi, Izquierdo, Walter, Martin, Harold et al. (2020). Deep convolutional neural network for mixed random impulse and Gaussian noise reduction in digital images . IET IMAGE PROCESSING, 14(15), 3791-3801. 10.1049/iet-ipr.2019.0931

Open Access

cited authors

  • Mafi, Mehdi; Izquierdo, Walter; Martin, Harold; Cabrerizo, Mercedes; Adjouadi, Malek

authors

publication date

  • December 15, 2020

published in

keywords

  • 20-layer network
  • CLASSIFICATION
  • CNN-based approach
  • Computer Science
  • Computer Science, Artificial Intelligence
  • DOMAIN
  • Engineering
  • Engineering, Electrical & Electronic
  • Gaussian noise
  • Gaussian noise reduction
  • Imaging Science & Photographic Technology
  • MIXTURE
  • REMOVAL ALGORITHM
  • RESTORATION
  • SPARSE
  • Science & Technology
  • Technology
  • additional 12 images
  • batch normalisation
  • convolution
  • convolutional neural network
  • deep CNN
  • different structural metrics
  • digital images
  • filtering theory
  • image classification
  • image denoising
  • image segmentation
  • known noise mixtures
  • learning (artificial intelligence)
  • minimal loss
  • mixed impulse
  • mixed random impulse
  • neural nets
  • optimal denoising results
  • optimal estimation
  • unknown noise mixtures

Digital Object Identifier (DOI)

publisher

  • WILEY

start page

  • 3791

end page

  • 3801

volume

  • 14

issue

  • 15