Sub-network modeling and integration for low-light enhancement of aerial images-Reference-Cited by-同舟云学术

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Sub-network modeling and integration for low-light enhancement of aerial images

Published:2023-09-09 Issue:11 Volume:55 Page:
ISSN:0306-8919
Container-title:Optical and Quantum Electronics
language:en
Short-container-title:Opt Quant Electron

Author:

Uganya G.,Devi C. H. Sarada,Chaturvedi Abhay,Shankar B. B.,Ramesh Janjhyam Venkata Naga,Kiran Ajmeera

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Link

https://link.springer.com/content/pdf/10.1007/s11082-023-05224-7.pdf

Reference18 articles.

1. Dai, Q., Pu, Y.-F., Rahman, Z., Aamir, M.: Fractional-order fusion model for low-light image enhancement. Symmetry 11, 574 (2019). https://doi.org/10.3390/sym11040574

2. Dhas, D.S.E.J., Raja, R., Jannet, S., Wins, K.L.D., Thomas, J.M., Kandavalli, S.R.: Effect of carbide ceramics and coke on the properties of dispersion strengthened aluminium-silicon7-magnesium hybrid composites. Materialwiss. Werkstofftech. 54(2), 147–157 (2023)

3. Jiang, Y. et al.: Enlightengan: deep light enhancement without paired supervision. (2019), arXiv:1906.06972

4. Li, H., He, X., Tao, D., Tang, Y., Wang, R.: Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning. Pattern Recognit. 79, 130–146 (2018)

5. Li, J., Feng, X., Fan, H.: Saliency-based image correction for colorblind patients. Comput. vis. Media 6, 169–189 (2020). https://doi.org/10.1007/s41095-020-0172-x

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