Research on the perception method of tiny objects in low-light and wide-field video
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41598-024-68129-9.pdf
Reference27 articles.
1. Zeng, B. -W. and K. T. U. Low-light image enhancement algorithm based on lime with pre-processing and post-processing. In 2020 International Conference on Wavelet Analysis and Pattern Recognition, pp. 96–101 (2020).
2. Ren, X., Yang, W., Cheng, W.-H. & Liu, J. LR3M: Robust low-light enhancement via low-rank regularized retinex model. IEEE Trans. Image Process. 29, 5862–5876 (2020).
3. Zhang, Y., Zhang, J. and Gu, X. Kindling the darkness: A practical low-light image enhancer. In Proc. of the 27th ACM International Conference on Multimedia. Association for Computing Machinery, 1632–1640 (2019).
4. Gao, F., Dong, J., Li, B. & Xu, Q. Automatic change detection in synthetic aperture radar images based on PCANet. IEEE Geosci. Remote Sens. Lett. 13(12), 1792–1796 (2016).
5. Liu, J., Gong, M., Qin, K. & Zhang, P. A deep convolutional coupling network for change detection based on heterogeneous optical and radar images. IEEE Trans. Neural Netw. Learn. Syst. 29(3), 545–559 (2018).
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