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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\u2010eye performance in noise types recognition. The authors\u2019 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.<\/jats:p>","DOI":"10.1049\/iet-ipr.2017.0374","type":"journal-article","created":{"date-parts":[[2017,8,22]],"date-time":"2017-08-22T22:12:29Z","timestamp":1503439949000},"page":"1238-1245","source":"Crossref","is-referenced-by-count":52,"title":["Image noise types recognition using convolutional neural network with principal components analysis"],"prefix":"10.1049","volume":"11","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-7186-7686","authenticated-orcid":false,"given":"Hui Ying","family":"Khaw","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering University of Malaya 50603 Kuala Lumpur Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-7593-5308","authenticated-orcid":false,"given":"Foo Chong","family":"Soon","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering University of Malaya 50603 Kuala Lumpur Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joon Huang","family":"Chuah","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering University of Malaya 50603 Kuala Lumpur Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chee\u2010Onn","family":"Chow","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering University of Malaya 50603 Kuala Lumpur Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2017,10,17]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2014.2381649"},{"key":"e_1_2_7_3_1","volume-title":"Handbook of image and video processing","author":"Bovik A.","year":"2005"},{"key":"e_1_2_7_4_1","volume-title":"Digital image processing","author":"Gonzalez R.C.","year":"2008"},{"issue":"9","key":"e_1_2_7_5_1","first-page":"4172","article-title":"Estimation of Gaussian, Poissonian\u2010Gaussian, and processed visual noise and its level function","volume":"25","author":"Rakhshanfar M.","year":"2016","journal-title":"IEEE Trans. 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