Perceiving and Modeling Density for Image Dehazing

Author:

Ye Tian,Zhang Yunchen,Jiang Mingchao,Chen Liang,Liu Yun,Chen Sixiang,Chen Erkang

Publisher

Springer Nature Switzerland

Reference24 articles.

1. Ancuti, C.O., Ancuti, C., Sbert, M., Timofte, R.: Dense haze: a benchmark for image dehazing with dense-haze and haze-free images. In: IEEE International Conference on Image Processing (ICIP). IEEE ICIP 2019 (2019)

2. Ancuti, C.O., Ancuti, C., Timofte, R., Vleeschouwer, C.D.: O-haze: a dehazing benchmark with real hazy and haze-free outdoor images. In: IEEE Conference on Computer Vision and Pattern Recognition, NTIRE Workshop. NTIRE CVPR’18 (2018)

3. Ancuti, C.O., Ancuti, C., Vasluianu, F.A., Timofte, R.: Ntire 2020 challenge on nonhomogeneous dehazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 490–491 (2020)

4. Ancuti, C.O., Ancuti, C., Vasluianu, F.A., Timofte, R.: Ntire 2021 nonhomogeneous dehazing challenge report. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 627–646 (2021)

5. Berman, D., Avidan, S., et al.: Non-local image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1674–1682 (2016)

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