Lightweight Separable Convolutional Dehazing Network to Mobile FPGA
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50078-7_10
Reference28 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: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1014–1018. IEEE (2019)
2. Ancuti, C.O., Ancuti, C., Timofte, R., De Vleeschouwer, C.: O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 754–762 (2018)
3. Ancuti, C., Ancuti, C.O., De Vleeschouwer, C.: D-HAZY: a dataset to evaluate quantitatively dehazing algorithms. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 2226–2230. IEEE (2016)
4. Lecture Notes in Computer Science;C Ancuti,2018
5. Berman, D., Avidan, S.: Non-local image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1674–1682 (2016)
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