HDUD-Net: heterogeneous decoupling unsupervised dehaze network
Author:
Funder
Beijing Natural Science Foundation
General Program of Beijing Municipal Education Commission
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09199-0.pdf
Reference56 articles.
1. Yang Y, Wang C, Liu R, Zhang L, Guo X, Tao D (2022) Self-augmented unpaired image dehazing via density and depth decomposition. In: 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 2027–2036
2. Wu Y, Tao D, Zhan Y, Zhang C (2022) Bin-flow: bidirectional normalizing flow for robust image dehazing. IEEE Trans Image Process 31:6635–6648
3. Chen J, Yang G, Xia M, Zhang D (2022) From depth-aware haze generation to real-world haze removal. Neural Comput Appl 1–13
4. Gao X, Tang P, Cheng Q, Li J (2022) Air infrared small target local dehazing based on multiple-factor fusion cascade network. Neural Comput Appl 1–9
5. Sun H, Zhang Y, Chen P, Dan Z, Sun S, Wan J, Li W (2021) Scale-free heterogeneous cyclegan for defogging from a single image for autonomous driving in fog. Neural Comput Appl 1–15
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