Fetching the paper…
Reading the bibliography…
Universal adverse weather removal (UAWR) seeks to address various weather degradations within a unified framework.
P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE transactions on pattern analysis and machine intelligence , vol. 33, no. 5, pp. 898–916, 2010
2010
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.
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.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1125–1134
2017
Earlier work this paper cites.
X. Fu, J. Huang, X. Ding, Y. Liao, and J. Paisley, “Clearing the skies: A deep network architecture for single-image rain removal,” IEEE transactions on image processing , vol. 26, no. 6, pp. 2944–2956, 2017
2017
Earlier work this paper cites.
W. Yang, R. T. Tan, J. Feng, J. Liu, Z. Guo, and S. Yan, “Deep joint rain detection and removal from a single image,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1357–1366
2017
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.
S. Nah, T. Hyun Kim, and K. Mu Lee, “Deep multi-scale convolutional neural network for dynamic scene deblurring,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3883–3891
2017
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 transactions on image processing , vol. 27, no. 6, pp. 3064–3073, 2018
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 Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2482–2491
2018
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 Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 254–269
2018
Earlier work this paper cites.
R. Li, R. T. Tan, and L.-F. Cheong, “Robust optical flow in rainy scenes,” in Proceedings of the European conference on computer vision , 2018, pp. 288–304
2018
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
Earlier work this paper cites.
2018
Earlier work this paper cites.
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
Earlier work this paper cites.
C. Wei, W. Wang, W. Yang, and J. Liu, “Deep retinex decomposition for low-light enhancement,” in Proceedings of the British Machine Vision Conference , 2018
2018
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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 1633–1642
2019
Earlier work this paper cites.
H. Zhang, V. Sindagi, and V. M. Patel, “Image de-raining using a conditional generative adversarial network,” IEEE transactions on circuits and systems for video technology , vol. 30, no. 11, pp. 3943–3956, 2019
2019
Earlier work this paper cites.
D. Chen, M. He, Q. Fan, J. Liao, L. Zhang, D. Hou, L. Yuan, and G. Hua, “Gated context aggregation network for image dehazing and deraining,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision . IEEE, 2019, pp. 1375–1383
2019
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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 3937–3946
2019
Earlier work this paper cites.
Y. Quan, S. Deng, Y. Chen, and H. Ji, “Deep learning for seeing through window with raindrops,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 2463–2471
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
T. Wang, X. Yang, K. Xu, S. Chen, Q. Zhang, and R. W. Lau, “Spatial attentive single-image deraining with a high quality real rain dataset,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 12 270–12 279
2019
Earlier work this paper cites.
Q. Fan, D. Chen, L. Yuan, G. Hua, N. Yu, and B. Chen, “A general decoupled learning framework for parameterized image operators,” IEEE transactions on pattern analysis and machine intelligence , vol. 43, no. 1, pp. 33–47, 2019
2019
Earlier work this paper cites.
R. Li, R. T. Tan, and L.-F. Cheong, “All in one bad weather removal using architectural search,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 3175–3185
2020
Earlier work this paper cites.
S. Deng, M. Wei, J. Wang, Y. Feng, L. Liang, H. Xie, F. L. Wang, and M. Wang, “Detail-recovery image deraining via context aggregation networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 14 560–14 569
2020
Earlier work this paper cites.
H. Wang, Q. Xie, Q. Zhao, and D. Meng, “A model-driven deep neural network for single image rain removal,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 3103–3112
2020
Earlier work this paper cites.
J. Pan, J. Dong, Y. Liu, J. Zhang, J. Ren, J. Tang, Y.-W. Tai, and M.-H. Yang, “Physics-based generative adversarial models for image restoration and beyond,” IEEE transactions on pattern analysis and machine intelligence , vol. 43, no. 7, pp. 2449–2462, 2020
2020
Earlier work this paper cites.
W.-T. Chen, H.-Y. Fang, J.-J. Ding, and S.-Y. Kuo, “Pmhld: Patch map-based hybrid learning dehazenet for single image haze removal,” IEEE transactions on image processing , vol. 29, pp. 6773–6788, 2020
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 Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 07, 2020, pp. 11 908–11 915
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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 2157–2167
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 Proceedings of the European conference on computer vision , 2020, pp. 754–770
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , pp. 6840–6851, 2020
2020
Earlier work this paper cites.
F. Yang, H. Yang, J. Fu, H. Lu, and B. Guo, “Learning texture transformer network for image super-resolution,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 5791–5800
2020
Earlier work this paper cites.
Y. Wei, Z. Zhang, Y. Wang, M. Xu, Y. Yang, S. Yan, and M. Wang, “Deraincyclegan: Rain attentive cyclegan for single image deraining and rainmaking,” IEEE transactions on image processing , vol. 30, pp. 4788–4801, 2021
2021
Earlier work this paper cites.
H. Chen, Y. Wang, T. Guo, C. Xu, Y. Deng, Z. Liu, S. Ma, C. Xu, C. Xu, and W. Gao, “Pre-trained image processing transformer,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 12 299–12 310
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in Proceedings of the International conference on machine learning , 2021, pp. 8748–8763
2021
Cited alongside, same era.
R. Quan, X. Yu, Y. Liang, and Y. Yang, “Removing raindrops and rain streaks in one go,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 9147–9156
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 Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 4196–4205
2021
Cited alongside, same era.
K. Zhang, R. Li, Y. Yu, W. Luo, and C. Li, “Deep dense multi-scale network for snow removal using semantic and depth priors,” IEEE transactions on image processing , vol. 30, pp. 7419–7431, 2021
S. Yang, T. Wu, S. Shi, S. Lao, Y. Gong, M. Cao, J. Wang, and Y. Yang, “Maniqa: Multi-dimension attention network for no-reference image quality assessment,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 1191–1200
2022
Later among the works it cites.
H. Zhang, Y. Ba, E. Yang, V. Mehra, B. Gella, A. Suzuki, A. Pfahnl, C. C. Chandrappa, A. Wong, and A. Kadambi, “Weatherstream: Light transport automation of single image deweathering,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 13 499–13 509
2023
Later among the works it cites.
Y. Li, J. Lu, H. Chen, X. Wu, and X. Chen, “Dilated convolutional transformer for high-quality image deraining,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 4198–4206
2023
Later among the works it cites.
X. Chen, H. Li, M. Li, and J. Pan, “Learning a sparse transformer network for effective image deraining,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 5896–5905
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
Q. Yi, J. Li, Q. Dai, F. Fang, G. Zhang, and T. Zeng, “Structure-preserving deraining with residue channel prior guidance,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 4238–4247
2021
Cited alongside, same era.
M.-W. Shao, L. Li, D.-Y. Meng, and W.-M. Zuo, “Uncertainty guided multi-scale attention network for raindrop removal from a single image,” IEEE transactions on image processing , vol. 30, pp. 4828–4839, 2021
2021
Cited alongside, same era.
K. Zhang, D. Li, W. Luo, and W. Ren, “Dual attention-in-attention model for joint rain streak and raindrop removal,” IEEE transactions on image processing , vol. 30, pp. 7608–7619, 2021
2021
Cited alongside, same era.
P. Esser, R. Rombach, and B. Ommer, “Taming transformers for high-resolution image synthesis,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 12 873–12 883
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. Le, Y.-H. Sung, Z. Li, and T. Duerig, “Scaling up visual and vision-language representation learning with noisy text supervision,” in Proceedings of the international conference on machine learning . PMLR, 2021, pp. 4904–4916
2021
Cited alongside, same era.
K. Jiang, Z. Wang, P. Yi, C. Chen, Z. Wang, X. Wang, J. Jiang, and C.-W. Lin, “Rain-free and residue hand-in-hand: A progressive coupled network for real-time image deraining,” IEEE transactions on image processing , vol. 30, pp. 7404–7418, 2021
2021
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Li, Y. Fan, X. Xiang, D. Demandolx, R. Ranjan, R. Timofte, and L. Van Gool, “Efficient and explicit modelling of image hierarchies for image restoration,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 18 278–18 289
2023
Later among the works it cites.
D. Park, B. H. Lee, and S. Y. Chun, “All-in-one image restoration for unknown degradations using adaptive discriminative filters for specific degradations,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 5815–5824
2023
Later among the works it cites.
P. W. Patil, S. Gupta, S. Rana, S. Venkatesh, and S. Murala, “Multi-weather image restoration via domain translation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2023, pp. 21 696–21 705
2023
Later among the works it cites.
T. Ye, S. Chen, J. Bai, J. Shi, C. Xue, J. Jiang, J. Yin, E. Chen, and Y. Liu, “Adverse weather removal with codebook priors,” in Proceedings of the IEEE/CVF international conference on computer vision , 2023, pp. 12 653–12 664
2023
Later among the works it cites.
O. Özdenizci and R. Legenstein, “Restoring vision in adverse weather conditions with patch-based denoising diffusion models,” IEEE transactions on pattern analysis and machine intelligence , 2023
2023
Later among the works it cites.
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
Later among the works it cites.
C. Yu, S. Chen, Y. Chang, Y. Song, and L. Yan, “Both diverse and realism matter: Physical attribute and style alignment for rainy image generation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2023, pp. 12 387–12 397
2023
Later among the works it cites.
Q. Wang, K. Jiang, Z. Wang, W. Ren, J. Zhang, and C.-W. Lin, “Multi-scale fusion and decomposition network for single image deraining,” IEEE transactions on image processing , vol. 33, pp. 191–204, 2023
2023
Later among the works it cites.
S. Chen, T. Ye, C. Xue, H. Chen, Y. Liu, E. Chen, and L. Zhu, “Uncertainty-driven dynamic degradation perceiving and background modeling for efficient single image desnowing,” in Proceedings of the ACM international conference on multimedia , 2023, pp. 4269–4280
2023
Later among the works it cites.
S. Chen, T. Ye, Y. Liu, J. Bai, H. Chen, Y. Lin, J. Shi, and E. Chen, “Cplformer: Cross-scale prototype learning transformer for image snow removal,” in Proceedings of the ACM international conference on multimedia , 2023, pp. 4228–4239
2023
Later among the works it cites.
T. Zhang, N. Jiang, H. Wu, K. Zhang, Y. Niu, and T. Zhao, “Hcsd-net: Single image desnowing with color space transformation,” in Proceedings of the ACM international conference on multimedia , 2023, pp. 8125–8133
2023
Later among the works it cites.
Y. Zhu, T. Wang, X. Fu, X. Yang, X. Guo, J. Dai, Y. Qiao, and X. Hu, “Learning weather-general and weather-specific features for image restoration under multiple adverse weather conditions,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 21 747–21 758
2023
Later among the works it cites.
Z. Liang, C. Li, S. Zhou, R. Feng, and C. C. Loy, “Iterative prompt learning for unsupervised backlit image enhancement,” in Proceedings of the IEEE/CVF international conference on computer vision , 2023, pp. 8094–8103
2023
Later among the works it cites.
V. Potlapalli, S. W. Zamir, S. Khan, and F. Khan, “Promptir: Prompting for all-in-one image restoration,” in Proceedings of the advances in neural information processing systems , 2023
2023
Later among the works it cites.
W. Zou, H. Gao, L. Chen, Y. Zhang, M. Jiang, Z. Yu, and M. Tan, “Cross-view hierarchy network for stereo image super-resolution,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 1396–1405
2023
Later among the works it cites.
J. Zhang, J. Huang, M. Yao, Z. Yang, H. Yu, M. Zhou, and F. Zhao, “Ingredient-oriented multi-degradation learning for image restoration,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 5825–5835
2023
Later among the works it cites.
X. Lai, Z. Tian, Y. Chen, Y. Li, Y. Yuan, S. Liu, and J. Jia, “Lisa: Reasoning segmentation via large language model,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2024, pp. 9579–9589
2024
Later among the works it cites.
Z. Luo, F. K. Gustafsson, Z. Zhao, J. Sjölund, and T. B. Schön, “Controlling vision-language models for universal image restoration,” in Proceedings of the international conference on learning representations , 2024
2024
Later among the works it cites.
H. Yang, L. Pan, Y. Yang, and W. Liang, “Language-driven all-in-one adverse weather removal,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2024, pp. 24 902–24 912
2024
Later among the works it cites.
Z. Tan, Y. Wu, Q. Liu, Q. Chu, L. Lu, J. Ye, and N. Yu, “Exploring the application of large-scale pre-trained models on adverse weather removal,” IEEE transactions on image processing , 2024
2024
Later among the works it cites.
S. Chen, T. Ye, K. Zhang, Z. Xing, Y. Lin, and L. Zhu, “Teaching tailored to talent: Adverse weather restoration via prompt pool and depth-anything constraint,” in Proceedings of the European conference on computer vision , 2024, pp. 95–115
2024
Later among the works it cites.
C. Wang, J. Pan, W. Lin, J. Dong, W. Wang, and X.-M. Wu, “Selfpromer: Self-prompt dehazing transformers with depth-consistency,” in Proceedings of the AAAI conference on artificial intelligence , 2024, pp. 5327–5335
2024
Later among the works it cites.
Y. Zhang, S. Zhou, and H. Li, “Depth information assisted collaborative mutual promotion network for single image dehazing,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2024, pp. 2846–2855
2024
Later among the works it cites.
T. Ye, S. Chen, W. Chai, Z. Xing, J. Qin, G. Lin, and L. Zhu, “Learning diffusion texture priors for image restoration,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2024, pp. 2524–2534
2024
Later among the works it cites.
R. Wu, T. Yang, L. Sun, Z. Zhang, S. Li, and L. Zhang, “Seesr: Towards semantics-aware real-world image super-resolution,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2024, pp. 25 456–25 467
2024
Later among the works it cites.
G. Wu, J. Jiang, K. Jiang, and X. Liu, “Harmony in diversity: Improving all-in-one image restoration via multi-task collaboration,” in Proceedings of the 32nd ACM international conference on multimedia , 2024, pp. 6015–6023
2024
Later among the works it cites.
T. Wang, K. Zhang, Z. Shao, W. Luo, B. Stenger, T. Lu, T.-K. Kim, W. Liu, and H. Li, “Gridformer: Residual dense transformer with grid structure for image restoration in adverse weather conditions,” International journal of computer vision , vol. 132, pp. 4541–4563, 2024
2024
Later among the works it cites.
M. V. Conde, G. Geigle, and R. Timofte, “Instructir: High-quality image restoration following human instructions,” in Proceedings of the European conference on computer vision , 2024
2024
Later among the works it cites.
M. Yao, R. Xu, Y. Guan, J. Huang, and Z. Xiong, “Neural degradation representation learning for all-in-one image restoration,” IEEE transactions on image processing , 2024
2024
Later among the works it cites.
D. Zheng, X.-M. Wu, S. Yang, J. Zhang, J.-F. Hu, and W.-S. Zheng, “Selective hourglass mapping for universal image restoration based on diffusion model,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2024, pp. 25 445–25 455
2024
Later among the works it cites.
Y. Yang, C. Wang, R. Liu, L. Zhang, X. Guo, and D. Tao, “Self-augmented unpaired image dehazing via density and depth decomposition,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 2037–2046
2046
Closest in time.