Fetching the paper…
Reading the bibliography…
Adverse weather removal tasks like deraining, desnowing, and dehazing are usually treated as separate tasks.
J. Ma, Z. Zhao, X. Yi, J. Chen, L. Hong, and E. H. Chi, “Modeling task relationships in multi-task learning with multi-gate mixture-of-experts,” in ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining , 2018, pp. 1930–1939
1939
Earlier work this paper cites.
2014
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2015, pp. 1–9
2015
Earlier work this paper cites.
J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in Eur. Conf. Comput. Vis. Springer, 2016, pp. 694–711
2016
Earlier work this paper cites.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2016, pp. 3213–3223
2016
Earlier work this paper cites.
Y. Song, J. Li, X. Wang, and X. Chen, “Single image dehazing using ranking convolutional neural network,” IEEE Trans. Multimedia , vol. 20, no. 6, pp. 1548–1560, 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Adv. Neural Inform. Process. Syst. , vol. 30, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
X. Li, J. Wu, Z. Lin, H. Liu, and H. Zha, “Recurrent squeeze-and-excitation context aggregation net for single image deraining,” in Eur. Conf. Comput. Vis. , 2018, pp. 254–269
2018
Earlier work this paper cites.
Y.-F. Liu, D.-W. Jaw, S.-C. Huang, and J.-N. Hwang, “Desnownet: Context-aware deep network for snow removal,” IEEE Trans. Image Process. , vol. 27, no. 6, pp. 3064–3073, 2018
2018
Earlier work this paper cites.
C. Sakaridis, D. Dai, S. Hecker, and L. Van Gool, “Model adaptation with synthetic and real data for semantic dense foggy scene understanding,” in Eur. Conf. Comput. Vis. , 2018, pp. 687–704
2018
Earlier work this paper cites.
R. Qian, R. T. Tan, W. Yang, J. Su, and J. Liu, “Attentive generative adversarial network for raindrop removal from a single image,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2018, pp. 2482–2491
2018
Earlier work this paper cites.
Y.-F. Liu, D.-W. Jaw, S.-C. Huang, and J.-N. Hwang, “Desnownet: Context-aware deep network for snow removal,” IEEE Trans. Image Process. , vol. 27, no. 6, pp. 3064–3073, 2018
2018
Earlier work this paper cites.
D. Ren, W. Zuo, Q. Hu, P. Zhu, and D. Meng, “Progressive image deraining networks: A better and simpler baseline,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2019, pp. 3937–3946
2019
Earlier work this paper cites.
X. Liu, Y. Ma, Z. Shi, and J. Chen, “Griddehazenet: Attention-based multi-scale network for image dehazing,” in Int. Conf. Comput. Vis. , 2019, pp. 7314–7323
2019
Earlier work this paper cites.
H.-M. Hu, H. Zhang, Z. Zhao, B. Li, and J. Zheng, “Adaptive single image dehazing using joint local-global illumination adjustment,” IEEE Trans. Multimedia , vol. 22, no. 6, pp. 1485–1495, 2019
2019
Earlier work this paper cites.
X. Hu, C.-W. Fu, L. Zhu, and P.-A. Heng, “Depth-attentional features for single-image rain removal,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2019, pp. 8022–8031
2019
Earlier work this paper cites.
R. Li, L.-F. Cheong, and R. T. Tan, “Heavy rain image restoration: Integrating physics model and conditional adversarial learning,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2019, pp. 1633–1642
2019
Earlier work this paper cites.
K. Jiang, Z. Wang, P. Yi, C. Chen, B. Huang, Y. Luo, J. Ma, and J. Jiang, “Multi-scale progressive fusion network for single image deraining,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020, pp. 8346–8355
2020
Earlier work this paper cites.
Y. Wang, D. Gong, J. Yang, Q. Shi, D. Xie, B. Zeng et al. , “Deep single image deraining via modeling haze-like effect,” IEEE Trans. Multimedia , vol. 23, pp. 2481–2492, 2020
2020
Earlier work this paper cites.
H. Dong, J. Pan, L. Xiang, Z. Hu, X. Zhang, F. Wang, and M.-H. Yang, “Multi-scale boosted dehazing network with dense feature fusion,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020, pp. 2157–2167
2020
Earlier work this paper cites.
X. Qin, Z. Wang, Y. Bai, X. Xie, and H. Jia, “Ffa-net: Feature fusion attention network for single image dehazing,” in AAAI Conf. Artif. Intell. , vol. 34, 2020, pp. 11 908–11 915
2020
Earlier work this paper cites.
W.-T. Chen, H.-Y. Fang, J.-J. Ding, C.-C. Tsai, and S.-Y. Kuo, “Jstasr: Joint size and transparency-aware snow removal algorithm based on modified partial convolution and veiling effect removal,” in Eur. Conf. Comput. Vis. , 2020, pp. 754–770
2020
Earlier work this paper cites.
R. Li, R. T. Tan, and L.-F. Cheong, “All in one bad weather removal using architectural search,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020, pp. 3175–3185
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020, pp. 9729–9738
2020
Cited alongside, same era.
W.-T. Chen, Z.-K. Huang, C.-C. Tsai, H.-H. Yang, J.-J. Ding, and S.-Y. Kuo, “Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2022, pp. 17 653–17 662
2022
Later among the works it cites.
A. Kulkarni, P. W. Patil, S. Murala, and S. Gupta, “Unified multi-weather visibility restoration,” IEEE Trans. Multimedia , 2022
2022
Later among the works it cites.
J. Han, W. Li, P. Fang, C. Sun, J. Hong, M. A. Armin, L. Petersson, and H. Li, “Blind image decomposition,” in Eur. Conf. Comput. Vis. , 2022, pp. 218–237
2022
Later among the works it cites.
K. Han, Y. Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y. Tang, A. Xiao, C. Xu, Y. Xu et al. , “A survey on vision transformer,” IEEE Trans. Pattern Anal. Mach. Intell. , 2022
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, “Supervised contrastive learning,” Adv. Neural Inform. Process. Syst. , vol. 33, pp. 18 661–18 673, 2020
2020
Cited alongside, same era.
L. Yu, B. Wang, J. He, G.-S. Xia, and W. Yang, “Single image deraining with continuous rain density estimation,” IEEE Trans. Multimedia , vol. 25, pp. 443–456, 2021
2021
Cited alongside, same era.
Y. Yang, J. Guan, S. Huang, W. Wan, Y. Xu, and J. Liu, “End-to-end rain removal network based on progressive residual detail supplement,” IEEE Trans. Multimedia , vol. 24, pp. 1622–1636, 2021
2021
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 IEEE Conf. Comput. Vis. Pattern Recog. , 2021, pp. 10 551–10 560
2021
Cited alongside, same era.
J. Shin, H. Park, and J. Paik, “Region-based dehazing via dual-supervised triple-convolutional network,” IEEE Trans. Multimedia , vol. 24, pp. 245–260, 2021
2021
Cited alongside, same era.
W.-T. Chen, H.-Y. Fang, C.-L. Hsieh, C.-C. Tsai, I. Chen, J.-J. Ding, S.-Y. Kuo et al. , “All snow removed: Single image desnowing algorithm using hierarchical dual-tree complex wavelet representation and contradict channel loss,” in Int. Conf. Comput. Vis. , 2021, pp. 4196–4205
2021
Cited alongside, same era.
S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, M.-H. Yang, and L. Shao, “Multi-stage progressive image restoration,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2021, pp. 14 821–14 831
2021
Cited alongside, same era.
J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, and R. Timofte, “Swinir: Image restoration using swin transformer,” in Int. Conf. Comput. Vis. Workshops , 2021, pp. 1833–1844
2021
Cited alongside, same era.
2022
Later among the works it cites.
W.-T. Chen, Z.-K. Huang, C.-C. Tsai, H.-H. Yang, J.-J. Ding, and S.-Y. Kuo, “Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2022, pp. 17 653–17 662
2022
Later among the works it cites.
Z. Wang, X. Cun, J. Bao, W. Zhou, J. Liu, and H. Li, “Uformer: A general u-shaped transformer for image restoration,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2022, pp. 17 683–17 693
2022
Later among the works it cites.
X. Zhang, H. Zeng, S. Guo, and L. Zhang, “Efficient long-range attention network for image super-resolution,” in Eur. Conf. Comput. Vis. , 2022
2022
Later among the works it cites.
J. Cao, J. Liang, K. Zhang, Y. Li, Y. Zhang, W. Wang, and L. V. Gool, “Reference-based image super-resolution with deformable attention transformer,” in Eur. Conf. Comput. Vis. , 2022
2022
Later among the works it cites.
Z. Chen, Y. Zhang, J. Gu, Y. Zhang, L. Kong, and X. Yuan, “Cross aggregation transformer for image restoration,” in Adv. Neural Inform. Process. Syst. , 2022
2022
Later among the works it cites.
J. Xiao, X. Fu, F. Wu, and Z.-J. Zha, “Stochastic window transformer for image restoration,” in Adv. Neural Inform. Process. Syst. , 2022
2022
Later among the works it cites.
J. Xiao, X. Fu, A. Liu, F. Wu, and Z.-J. Zha, “Image de-raining transformer,” IEEE Trans. Pattern Anal. Mach. Intell. , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
Z. Liu, H. Hu, Y. Lin, Z. Yao, Z. Xie, Y. Wei, J. Ning, Y. Cao, Z. Zhang, L. Dong et al. , “Swin transformer v2: Scaling up capacity and resolution,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2022, pp. 12 009–12 019
2022
Later among the works it cites.
2022
Later among the works it cites.
M. Emad, M. Peemen, and H. Corporaal, “Moesr: blind super-resolution using kernel-aware mixture of experts,” in IEEE Winter Conf. Appl. Comput. Vis. , 2022, pp. 3408–3417
2022
Later among the works it cites.
J. Liang, H. Zeng, and L. Zhang, “Efficient and degradation-adaptive network for real-world image super-resolution,” in Eur. Conf. Comput. Vis. , 2022, pp. 574–591
2022
Later among the works it cites.
S. Ren, D. Zhou, S. He, J. Feng, and X. Wang, “Shunted self-attention via multi-scale token aggregation,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2022, pp. 10 853–10 862
2022
Later among the works it cites.
H. Liu, Y. Sun, Y. Bandoh, M. Kitahara, and S. Satoh, “Deep counterfactual representation learning for visual recognition against weather corruptions,” IEEE Trans. Multimedia , 2023
2023
Closest in time.
X. Chen, J. Pan, J. Lu, Z. Fan, and H. Li, “Hybrid cnn-transformer feature fusion for single image deraining,” in AAAI Conf. Artif. Intell. , 2023, pp. 378–386
2023
Closest in time.
X. Chen, H. Li, M. Li, and J. Pan, “Learning a sparse transformer network for effective image deraining,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2023, pp. 5896–5905
2023
Closest in time.
Y. Song, Z. He, H. Qian, and X. Du, “Vision transformers for single image dehazing,” IEEE Trans. Image Process. , vol. 32, pp. 1927–1941, 2023
2023
Closest in time.