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

Content Provider	IET Digital Library
Author	Thakur, Rini Smita Yadav, Ram Narayan Gupta, Lalita
Abstract	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.
Starting Page	2367
Ending Page	2380
Page Count	14
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 13, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0157
Journal	IET Image Processing
Publisher Date	2019-08-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Blind Gaussian Denoising Block-matching CNN Based Image Denoising Model CNN Model Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Deep Neural Network Denoising Performance DnCNN-B DnCNN-S Gaussian Noise Image Denoising Image Restoration Image Segmentation Learning in AI Markov Random Field Approach Neural Computing Technique NN3D NonCNN Method Object Classification Object Segmentation Optical, Image And Video Signal Processing PDNN Prior Driven Network Residual Learning Based Model Statistics Three-dimensional Filtering Wavelet Random Field
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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