Open AccessDeep convolutional neural network for mixed random impulse and Gaussian noise reduction in digital images
Author(s) -
Mafi Mehdi,
Izquierdo Walter,
Martin Harold,
Cabrerizo Mercedes,
Adjouadi Malek
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0931
Subject(s) - computer science , convolutional neural network , noise reduction , artificial intelligence , impulse noise , gaussian noise , pattern recognition (psychology) , gaussian , noise (video) , deep learning , segmentation , image (mathematics) , pixel , physics , quantum mechanics
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 structural metrics when dealing with both known and unknown noise mixtures. Moreover, a comprehensive comparison of denoising filters through the use of different structural metrics is provided to highlight the merits of the proposed approach. Optimal denoising results were obtained by using a 20‐layer network with 40 × 40 patches trained on 400 180 × 180 images from the Berkeley segmentation data set (BSD) and tested on the BSD100 data set and an additional 12 images of general interest to the research community. The comparative results provide credence to the merits of the proposed filter and the comprehensive assessment of results highlights the novelty and performance of this CNN‐based approach.