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

Content Provider	IET Digital Library
Author	Mafi, Mehdi Izquierdo, Walter Martin, Harold Cabrerizo, Mercedes Adjouadi, Malek
Abstract	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.
Starting Page	3791
Ending Page	3801
Page Count	11
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 15, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0931
Journal	IET Image Processing
Publisher Date	2020-12-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	20-layer Network Additional 12 Image Batch Normalisation CNN-based Approach Computer Vision And Image Processing Technique Convolution Convolutional Neural Network Deep CNN Different Structural Metrics Digital Image Filtering Method in Signal Processing Filtering Theory Gaussian Noise Gaussian Noise Reduction Image Classification Image Denoising Image Segmentation Knowledge Engineering Technique Known Noise Mixtures Learning in AI Minimal Loss Mixed Impulse Mixed Random Impulse Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Optimal Denoising Result Optimal Estimation Unknown Noise Mixtures
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Enhanced CNN for image denoising

Deep CNN for removal of salt and pepper noise

Survey on mixed impulse and Gaussian denoising filters

PReLU and edge-aware filter-based image denoiser using convolutional neural network

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

High-density impulse noise detection and removal using deep convolutional neural network with particle swarm optimisation

Convolutional neural network for smooth filtering detection

ResDNN: deep residual learning for natural image denoising

Modulation classification based on denoising autoencoder and convolutional neural network with GNU radio

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

Similar Documents

Enhanced CNN for image denoising

Deep CNN for removal of salt and pepper noise

Survey on mixed impulse and Gaussian denoising filters

PReLU and edge-aware filter-based image denoiser using convolutional neural network

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

High-density impulse noise detection and removal using deep convolutional neural network with particle swarm optimisation

Convolutional neural network for smooth filtering detection

ResDNN: deep residual learning for natural image denoising

Modulation classification based on denoising autoencoder and convolutional neural network with GNU radio

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