VBNC: Voting Based Noise Classification Framework Using Deep CNN-Reference-Cited by-同舟云学术

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VBNC: Voting Based Noise Classification Framework Using Deep CNN

Published:2021 Issue: Volume: Page:357-363
ISSN:2367-3370
Container-title:Conference Proceedings of ICDLAIR2019
language:
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Author:

Kumain Sandeep Chand,Kumar Kamal

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-67187-7_37

Reference13 articles.

1. Sampat, M.P., Markey, M.K., Bovik, A.C.: Computer-aided detection and diagnosis in mammography. Handbook of image and video processing 2(1), 1195–1217 (2005)

2. Gonzalez, R.C., Woods, R.E.: ‘Digital image processing’ (Pearson Education, 1992, 2008, 3rd edn.)

3. Beaton, R. J. (1983, October). Quantitative models of image quality. In Proceedings of the Human Factors Society Annual Meeting (Vol. 27, No. 1, pp. 41-45). Sage CA: Los Angeles, CA: SAGE Publications

4. Khaw, H.Y., Soon, F.C., Chuah, J.H., Chow, C.O.: Image noise types recognition using convolutional neural network with principal components analysis. IET Image Processing 11(12), 1238–1245 (2017)

5. Chuah, J. H., Khaw, H. Y., Soon, F. C., & Chow, C. O. (2017, November). Detection of Gaussian noise and its level using deep convolutional neural network. In TENCON 2017-2017 IEEE Region 10 Conference (pp. 2447-2450). IEEE

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2. A Deep Learning Approach for Gaussian Noise-Level Quantification;Lecture Notes in Electrical Engineering;2023

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