Open Access
Issue
JNWPU
Volume 39, Number 4, August 2021
Page(s) 901 - 908
DOI https://doi.org/10.1051/jnwpu/20213940901
Published online 23 September 2021
  1. Wu Di, Zhu Qingsong. The latest research progress of image dehazing[J]. Acta Automatica Sinica, 2015, 41(2): 221–239 [Article] (in Chinese) [NASA ADS] [Google Scholar]
  2. Stark J A. Adaptive image contrast enhancement using generalizations of histogram equalization[J]. IEEE Trans on Image Processing, 2000, 9(5): 889–896 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  3. Cooper T J, Baqai F A. Analysis and extensions of the Frankle-Mccann retinex algorithm[J]. Journal of Electronic Imaging, 2004, 13(1): 85–92 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  4. He K, Sun J, Fellow, et al. Single image haze removal using dark channel prior[J]. IEEE Trans on Pattern Analysis & Machine Intelligence, 2011, 33(12): 2341–2353 [Article] [CrossRef] [Google Scholar]
  5. Zhu Q, Mai J, Shao L. A Fast single image haze removal algorithm using color attenuation prior[J]. IEEE Trans on Image Processing, 2015, 24(11): 3522–3533 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  6. Cai B, Xu X, Jia K, et al. Dehaze net: an end-to-end system for single image haze removal[J]. IEEE Trans on Image Processing, 2016, 25(11): 5187–5198 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  7. Ren W, Liu S, Zhang H, et al. Single image dehazing via multi-scale convolutional neural networks[C]//European Conference on Computer Vision, 2016: 154–169 [Google Scholar]
  8. Li B, Peng X, Wang Z, et al. AOD-net: all-in-one dehazing network[C]//2017 IEEE International Conference on Computer Vision, 2017: 4770–4778 [Google Scholar]
  9. Li R, Pan J, Li Z, et al. Single image dehazing via conditional generative adversarial network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 8202–8211 [Google Scholar]
  10. Chen D, He M, Fan Q, et al. Gated context aggregation network for image dehazing and deraining[C]//2019 IEEE Winter Conference on Applications of Computer Vision, 2019: 1375–1383 [Google Scholar]
  11. Qu Y, Chen Y, Huang J, et al. Enhanced pix2pix dehazing network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019: 8160–8168 [Google Scholar]
  12. Shao Y, Li L, Ren W, et al. Domain adaptation for image dehazing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 2808–2817 [Google Scholar]
  13. Tian C, Xu Y, Li Z, et al. Attention-guided CNN for image denoising[J]. Neural Networks, 2020, 124: 117–129 [Article] [Google Scholar]
  14. Chen Y, Liu L, Phonevilay V, et al. Image super-resolution reconstruction based on feature map attention mechanism[J]. Applied Intelligence, 2021, 51(8): 1–14 [Article] [Google Scholar]
  15. Liao Y, Su Z, Liang X, et al. HDP-net: haze density prediction network for nighttime dehazing[C]//Pacific Rim Conference on Multimedia, 2018: 469–480 [Google Scholar]
  16. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015: 234–241 [Google Scholar]
  17. Wang Z, Ji S. Smoothed dilated convolutions for improved dense prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018: 2486–2495 [Google Scholar]
  18. Wang Q, Wu B, Zhu P, et al. ECA-net: Efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 11534–11542 [Google Scholar]
  19. Li B, Ren W, Fu D, et al. Benchmarking single-image dehazing and beyond[J]. IEEE Trans on Image Processing, 2018, 28(1): 492–505 [Google Scholar]

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