Real-time endoscopy haze removal: a synthetical method
Author:
Funder
Student Research Training Program
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16375-w.pdf
Reference17 articles.
1. Cai B, Xiangming X, Tao D (2016a) Real-time video Dehazing based on Spatio-temporal MRF. In: Lecture notes in computer science. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-48896-7
2. Cai B, Xu X, Jia K, Qing C, Tao D (2016b) DehazeNet: an end-to-end system for single image haze removal. IEEE Trans on Image Process 25:5187–5198. https://doi.org/10.1109/TIP.2016.2598681
3. He, K, Sun, J, Tang, X (2009) Single image haze removal using dark channel prior, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, 2009, pp. 1956–1963. https://doi.org/10.1109/CVPR.2009.5206515
4. Kim J-H, Jang W-D, Sim J-Y, Kim C-S (2013) Optimized contrast enhancement for real-time image and video dehazing. J Vis Commun Image Represent 24:410–425. https://doi.org/10.1016/j.jvcir.2013.02.004
5. Lee S, Yun S, Nam J-H, Won CS, Jung S-W (2016) A review on dark channel prior based image dehazing algorithms. J Image Video Proc 2016:4. https://doi.org/10.1186/s13640-016-0104-y
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