NDLI: Denoising convolutional neural network with mask for salt and pepper noise

Content Provider	IET Digital Library
Author	Chen, Jiuning Li, Fang
Abstract	In this study, the authors propose a new loss function for denoising convolutional neural network (DnCNN) for salt-and-pepper noise (SPN). Based on the motivation of utilising the mask of SPN, firstly from the usual SPN-denoising restoration equation, the authors establish a perfect restoration condition; the restored image is precisely the clean image if this condition holds. Then they design a mask-involved loss function to encourage the network to satisfy this condition in training progress. Experimental results demonstrate that compared with general DnCNN and other state-of-the-art SPN denoising methods, DnCNN equipped with the proposed loss function involving mask (MaskDnCNN) is more effective, robust and efficient.
Starting Page	2604
Ending Page	2613
Page Count	10
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.2019.0096
Journal	IET Image Processing
Publisher Date	2019-09-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Clean Image Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Denoising General DnCNN Image Denoising Image Restoration Mask-involved Loss Function Neural Computing Technique Optical, Image And Video Signal Processing Perfect Restoration Condition Salt-and-pepper Noise SPN Denoising Method Usual SPN-denoising Restoration Equation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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