What is it about?
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.
Photo by Hitesh Choudhary on Unsplash
Why is it important?
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.
Read the Original
This page is a summary of: Image Noise Types Recognition Using Convolutional Neural Network with Principal Components Analysis , IET Image Processing, August 2017, the Institution of Engineering and Technology (the IET), DOI: 10.1049/iet-ipr.2017.0374.
You can read the full text:
The following have contributed to this page