State-of-art analysis of image denoising methods using convolutional neural networks

Convolutional neural networks (CNNs) are deep neural networks that can be trained on large databases and show outstanding performance on object classification, segmentation, image denoising etc. In the past few years, several image denoising techniques have been developed to improve the quality of a...

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Bibliographic Details
Published in:IET image processing 2019-11, Vol.13 (13), p.2367-2380
Main Authors: Thakur, Rini Smita, Yadav, Ram Narayan, Gupta, Lalita
Format: Article
Language:English
Subjects:
blind Gaussian denoising
block‐matching
CNN based image denoising models
CNN models
convolutional neural nets
convolutional neural networks
deep neural networks
denoising performance
DnCNN‐B
DnCNN‐S
Gaussian noise
image denoising
image restoration
image segmentation
learning (artificial intelligence)
Markov random field approaches
NN3D
nonCNN methods
object classification
object segmentation
PDNN
prior driven network
residual learning based models
Review Article
three‐dimensional filtering
wavelet random field
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Summary:Convolutional neural networks (CNNs) are deep neural networks that can be trained on large databases and show outstanding performance on object classification, segmentation, image denoising etc. In the past few years, several image denoising techniques have been developed to improve the quality of an image. The CNN based image denoising models have shown improvement in denoising performance as compared to non-CNN methods like block-matching and three-dimensional (3D) filtering, contemporary wavelet and Markov random field approaches etc. which had remained state-of-the-art for years. This study provides a comprehensive study of state-of-the-art image denoising methods using CNN. The literature associated with different CNNs used for image restoration like residual learning based models (DnCNN-S, DnCNN-B, IDCNN), non-locality reinforced (NN3D), fast and flexible network (FFDNet), deep shrinkage CNN (SCNN), a model for mixed noise reduction, denoising prior driven network (PDNN) are reviewed. DnCNN-S and PDNN remove Gaussian noise of fixed level, whereas DnCNN-B, IDCNN, NN3D and SCNN are used for blind Gaussian denoising. FFDNet is used for spatially variant Gaussian noise. The performance of these CNN models is analysed on BSD-68 and Set-12 datasets. PDNN shows the best result in terms of PSNR for both BSD-68 and Set-12 datasets.
ISSN:1751-9659
1751-9667
DOI:10.1049/iet-ipr.2019.0157