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

Content Provider	IET Digital Library
Author	Khaw, Hui Ying Soon, Foo Chong Chuah, Joon Huang Chow, Chee Onn
Abstract	Most of the impulse denoisers are either median filter-based or fuzzy filter-based, which can only perform well in low noise conditions. This study presents an efficient convolutional neural network (CNN) with particle swarm optimisation (PSO) model for high-density impulse noise removal. The proposed high-density impulse noise detection and removal model mainly consists of two parts: the impulse noise removal and impulse noisy pixel detection for restoration. The authors’ model initially leverages the powerful ability of deep CNN architecture to separate noise from the noisy image, then adopts PSO to pinpoint the most optimised threshold values for detecting impulse noisy pixels. An ensemble of these algorithms is an intelligent and adaptive solution, producing a clean output while preserving significant pixel information. Targeting to solve high-density impulse noise problems, the authors have trained their model with a massive collection of natural images and 14 standard testing images are used for validation purposes. In order to validate the robustness of the proposed method, different levels of high-density impulse noise are considered. Based on the final denoised images, their model has proven its reliability, in terms of both visual quality and quantitative evaluation, on greyscale and colour images.
Starting Page	365
Ending Page	374
Page Count	10
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 2, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5776
Journal	IET Image Processing
Publisher Date	2018-10-31
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Colour Image Computer Vision And Image Processing Technique Convolutional Neural Network Deep CNN Architecture Deep Convolutional Neural Network Denoised Image Feedforward Neural Network Fuzzy Filter Greyscale Image High-density Impulse Noise Detection High-density Impulse Noise Problem Image Denoising Image Restoration Impulse Denoisers Impulse Noise Impulse Noise Removal Impulse Noisy Pixel Detection Low Noise Condition Median Filter Natural Image Collection Neural Computing Technique Optical, Image And Video Signal Processing Optimisation Technique Particle Swarm Optimisation Pixel Information PSO Quantitative Evaluation Visual Quality
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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