Single Image Dehazing Using Deep Belief Neural Networks to Reduce Computational Complexity-Reference-Cited by-同舟云学术

Single Image Dehazing Using Deep Belief Neural Networks to Reduce Computational Complexity

Published:2020 Issue: Volume: Page:1471-1478
ISSN:
Container-title:New Trends in Computational Vision and Bio-inspired Computing
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Author:

Manoharan J. Samuel,Jayaseelan G.

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-41862-5_151

Reference10 articles.

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2. Md. Imtiyaz Anwar. et al. (2017) “Visibility enhancement with single image fog removal scheme using a post-processing technique” in IEEE International Conference on Signal Processing and Integrated Networks (SPIN), pp. 1–6.

3. Roy Bhowmik, Sud and Charan Singh (2004), “Forecasting fog over Delhi: An objective method”, Mausam, 55(2):313–322.

4. Chian-Hung Yeh, Li Wei Kang, Ming Sui Lee and Cheng Yang Lin (2013), “Haze effect removal from image via haze density estimation in optical model”, Optical Express, 21(22):127 – 141.

5. Zhigang Ling et al.(2015), “Optimal transmission estimation via fog density perception for efficient single image defogging” in IEEE Transactions on Multimedia,20(7):1–13.

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