Blood Cell Image Denoising Based on Tunicate Rat Swarm Optimization with Median Filter-Reference-Cited by-同舟云学术

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Blood Cell Image Denoising Based on Tunicate Rat Swarm Optimization with Median Filter

Published:2022 Issue: Volume: Page:33-45
ISSN:2367-4512
Container-title:Evolutionary Computing and Mobile Sustainable Networks
language:
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Author:

Mohana Dhas M.,Suresh Singh N.

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-9605-3_3

Reference15 articles.

1. Ghose, S., Singh, N., and Singh, P.: Image denoising using Deep Learning: convolutional neural network. In: 2020 10th International Conference on Cloud Computing, Data Science and Engineering (Confluence) (2020). https://doi.org/10.1109/confluence47617.2020.9057895

2. Gondara, L.: Medical image denoising using convolutional denoising autoencoders. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) (2016). https://doi.org/10.1109/icdmw.2016.0041

3. Liu, Z., Yan, W.Q., and Yang, M.L.: Image denoising based on a CNN model. In: 2018 4th International Conference on Control, Automation and Robotics (ICCAR) (2018). https://doi.org/10.1109/iccar.2018.8384706

4. Milanfar, P.: A tour of modern image filtering: new insights and methods, both practical and theoretical. IEEE Signal Process.(Magazine) 30(1), 106–128 (2013)

5. Pan, T., Peng, D., Yang, W., Li, H.-C.: A filter for SAR image despeckling using pre-trained convolutional neural network model. Remote Sens. 11(20), 2379 (2019)

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