Open AccessImage noise types recognition using convolutional neural network with principal components analysis
Author(s) -
Khaw Hui Ying,
Soon Foo Chong,
Chuah Joon Huang,
Chow CheeOnn
Publication year - 2017
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0374
Subject(s) - computer science , artificial intelligence , pattern recognition (psychology) , impulse noise , noise (video) , principal component analysis , gaussian noise , convolutional neural network , backpropagation , artificial neural network , median filter , image noise , stochastic gradient descent , image processing , image (mathematics) , pixel
This study presents a model to effectively recognise image noise of different types and levels: impulse, Gaussian, Speckle and Poisson noise, and a mixture of multiple types of the noise. To classify image noise type, the convolutional neural network (CNN) method with backpropagation algorithm and stochastic gradient descent optimisation techniques are implemented. In order to reduce the training time and computational cost of the algorithm, the principal components analysis (PCA) filters generating strategy is deployed to obtain data adaptive filter banks. The authors validated their designed CNN with PCA for noise types recognition model with degraded images containing noise of single and combination of multiple types, with a total of 11,000 and 1650 datasets for training and testing purposes, respectively. The variety and complexity of data have never been addressed before in any other research work. The capability of their intelligent system in handling images degraded under this complicated environment has surpassed human‐eye performance in noise types recognition. The authors’ experiments have proven the reliability of the proposed noise types recognition model by having achieved an overall average accuracy of 99.3% while recognising eight classes of noise.