Fetching the paper…
Reading the bibliography…
Image dehazing is quite challenging in dense-haze scenarios, where quite less original information remains in the hazy image.
E. J. McCartney, “Optics of the atmosphere: scattering by molecules and particles,” New York , 1976
1976
Earlier work this paper cites.
S. G. Narasimhan and S. K. Nayar, “Vision and the atmosphere,” International journal of computer vision , vol. 48, no. 3, pp. 233–254, 2002
2002
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing , vol. 13, pp. 600–612, 2004
2004
Earlier work this paper cites.
——, “Single image dehazing,” ACM Transactions on Graphics (TOG) , vol. 27, no. 3, pp. 1–9, 2008
2008
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research , vol. 9, no. 11, 2008
2008
Earlier work this paper cites.
K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 33, no. 12, pp. 2341–2353, 2010
2010
Earlier work this paper cites.
A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Transactions on image processing , vol. 21, no. 12, pp. 4695–4708, 2012
2012
Earlier work this paper cites.
A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” IEEE Signal processing letters , vol. 20, no. 3, pp. 209–212, 2012
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
R. Fattal, “Dehazing using color-lines,” ACM Transactions on Graphics (TOG) , vol. 34, no. 1, pp. 1–14, 2014
2014
Earlier work this paper cites.
Q. Zhu, J. Mai, and L. Shao, “Single image dehazing using color attenuation prior.” in BMVC . Citeseer, 2014
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Advances in Neural Information Processing Systems , vol. 63, 2014
2014
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning . PMLR, 2015, pp. 2256–2265
2015
Earlier work this paper cites.
D. Berman, S. Avidan et al. , “Non-local image dehazing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 1674–1682
2016
Earlier work this paper cites.
C. Chen, M. N. Do, and J. Wang, “Robust image and video dehazing with visual artifact suppression via gradient residual minimization,” in Proceedings of the European Conference on Computer Vision . Springer, 2016, pp. 576–591
2016
Earlier work this paper cites.
B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, “DehazeNet: An end-to-end system for single image haze removal,” IEEE Transactions on Image Processing , vol. 25, no. 11, pp. 5187–5198, 2016
2016
Earlier work this paper cites.
W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M.-H. Yang, “Single image dehazing via multi-scale convolutional neural networks,” in Proceedings of the European Conference on Computer Vision . Springer, 2016, pp. 154–169
2016
Earlier work this paper cites.
C. Li, J. Guo, R. Cong, Y. Pang, and B. Wang, “Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior,” IEEE Transactions on Image Processing , vol. 25, no. 12, pp. 5664–5677, 2016
2016
Earlier work this paper cites.
A. Van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves et al. , “Conditional image generation with pixelcnn decoders,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
B. Li, X. Peng, Z. Wang, J. Xu, and D. Feng, “AOD-Net: All-in-one dehazing network,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 4770–4778
2017
Earlier work this paper cites.
J. Zhang, Y. Cao, S. Fang, Y. Kang, and C. Wen Chen, “Fast haze removal for nighttime image using maximum reflectance prior,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7418–7426
2017
Earlier work this paper cites.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
H. Zhang and V. M. Patel, “Densely connected pyramid dehazing network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3194–3203
2018
Cited alongside, same era.
R. Liu, X. Fan, M. Hou, Z. Jiang, Z. Luo, and L. Zhang, “Learning aggregated transmission propagation networks for haze removal and beyond,” IEEE transactions on neural networks and learning systems , vol. 30, no. 10, pp. 2973–2986, 2018
2018
Cited alongside, same era.
W. Ren, L. Ma, J. Zhang, J. Pan, X. Cao, W. Liu, and M.-H. Yang, “Gated fusion network for single image dehazing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3253–3261
2018
Cited alongside, same era.
D. P. Kingma and P. Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” Advances in neural information processing systems , vol. 31, 2018
2018
Cited alongside, same era.
H. Wu, Y. Qu, S. Lin, J. Zhou, R. Qiao, Z. Zhang, Y. Xie, and L. Ma, “Contrastive learning for compact single image dehazing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 10 551–10 560
2021
Later among the works it cites.
A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion probabilistic models,” in International Conference on Machine Learning . PMLR, 2021, pp. 8162–8171
2021
Later among the works it cites.
P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” Advances in Neural Information Processing Systems , vol. 34, pp. 8780–8794, 2021
2021
Later among the works it cites.
H. Li, J. Li, D. Zhao, and L. Xu, “Dehazeflow: Multi-scale conditional flow network for single image dehazing,” in Proceedings of the 29th ACM International Conference on Multimedia , 2021, pp. 2577–2585
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
B. Li, W. Ren, D. Fu, D. Tao, D. Feng, W. Zeng, and Z. Wang, “Benchmarking single-image dehazing and beyond,” IEEE Transactions on Image Processing , vol. 28, no. 1, pp. 492–505, 2018
2018
Cited alongside, same era.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 586–595
2018
Cited alongside, same era.
X. Liu, Y. Ma, Z. Shi, and J. Chen, “GridDehazeNet: Attention-based multi-scale network for image dehazing,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 7314–7323
2019
Cited alongside, same era.
T. Guo, X. Li, V. Cherukuri, and V. Monga, “Dense scene information estimation network for dehazing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2019, pp. 0–0
2019
Cited alongside, same era.
Y. Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
Y. Liu, J. Pan, J. Ren, and Z. Su, “Learning deep priors for image dehazing,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2492–2500
2019
Cited alongside, same era.
A. Razavi, A. Van den Oord, and O. Vinyals, “Generating diverse high-fidelity images with vq-vae-2,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
M. Fu, H. Liu, Y. Yu, J. Chen, and K. Wang, “DW-GAN: A discrete wavelet transform gan for nonhomogeneous dehazing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 203–212
2021
Later among the works it cites.
2021
Later among the works it cites.
Z. Chen, Y. Wang, Y. Yang, and D. Liu, “PSD: Principled synthetic-to-real dehazing guided by physical priors,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 7180–7189
2021
Later among the works it cites.
C.-L. Guo, Q. Yan, S. Anwar, R. Cong, W. Ren, and C. Li, “Image dehazing transformer with transmission-aware 3d position embedding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5812–5820
2022
Later among the works it cites.
H. Yu, N. Zheng, M. Zhou, J. Huang, Z. Xiao, and F. Zhao, “Frequency and spatial dual guidance for image dehazing,” 2022
2022
Later among the works it cites.
C. Saharia, J. Ho, W. Chan, T. Salimans, D. J. Fleet, and M. Norouzi, “Image super-resolution via iterative refinement,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Later among the works it cites.
J. Whang, M. Delbracio, H. Talebi, C. Saharia, A. G. Dimakis, and P. Milanfar, “Deblurring via stochastic refinement,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 293–16 303
2022
Later among the works it cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 10 684–10 695
2022
Later among the works it cites.
H. Yu, J. Huang, Y. Liu, Q. Zhu, M. Zhou, and F. Zhao, “Source-free domain adaptation for real-world image dehazing,” in Proceedings of the 30th ACM International Conference on Multimedia , 2022, p. 6645–6654
2022
Later among the works it cites.
H. Liu, Z. Wu, L. Li, S. Salehkalaibar, J. Chen, and K. Wang, “Towards multi-domain single image dehazing via test-time training,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5831–5840
2022
Later among the works it cites.
Y. Zhou, Z. Chen, P. Li, H. Song, C. P. Chen, and B. Sheng, “Fsad-net: feedback spatial attention dehazing network,” IEEE transactions on neural networks and learning systems , 2022
2022
Later among the works it cites.
G. Fan, M. Gan, B. Fan, and C. P. Chen, “Multiscale cross-connected dehazing network with scene depth fusion,” IEEE Transactions on Neural Networks and Learning Systems , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
A. Lugmayr, M. Danelljan, A. Romero, F. Yu, R. Timofte, and L. Van Gool, “Repaint: Inpainting using denoising diffusion probabilistic models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 11 461–11 471
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Zheng, J. Zhan, S. He, J. Dong, and Y. Du, “Curricular contrastive regularization for physics-aware single image dehazing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5785–5794
2023
Closest in time.
R.-Q. Wu, Z.-P. Duan, C.-L. Guo, Z. Chai, and C. Li, “Ridcp: Revitalizing real image dehazing via high-quality codebook priors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 22 282–22 291
2023
Closest in time.