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Recently, Transformer networks have demonstrated outstanding performance in the field of image restoration due to the global receptive field and adaptability to input.
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.
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
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 ECCV . Springer, 2016, pp. 154–169
2016
Earlier work this paper cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in CVPR , 2016, pp. 2818–2826
2016
Earlier work this paper cites.
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in CVPR , 2016, pp. 1874–1883
2016
Earlier work this paper cites.
F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” in ICLR , 2016
2016
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Sgdr: Stochastic gradient descent with warm restarts,” 2016
2016
Earlier work this paper cites.
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in ICCV , 2017, pp. 764–773
2017
Earlier work this paper cites.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in CVPR , 2017, pp. 1251–1258
2017
Earlier work this paper cites.
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,” NIPS , vol. 30, 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 CVPR , 2017, pp. 1357–1366
2017
Earlier work this paper cites.
X. Fu, J. Huang, D. Zeng, Y. Huang, X. Ding, and J. Paisley, “Removing rain from single images via a deep detail network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3855–3863
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.
S. Nah, T. Hyun Kim, and K. Mu Lee, “Deep multi-scale convolutional neural network for dynamic scene deblurring,” in CVPR , 2017, pp. 3883–3891
2017
Earlier work this paper cites.
H. Zhang and V. M. Patel, “Density-aware single image de-raining using a multi-stream dense network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 695–704
2018
Earlier work this paper cites.
K. Zhang, W. Luo, Y. Zhong, L. Ma, W. Liu, and H. Li, “Adversarial spatio-temporal learning for video deblurring,” IEEE Transactions on Image Processing , vol. 28, no. 1, pp. 291–301, 2018
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.
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.
C. O. Ancuti, C. Ancuti, R. Timofte, and C. De Vleeschouwer, “O-haze: a dehazing benchmark with real hazy and haze-free outdoor images,” in CVPRW , 2018, pp. 754–762
2018
Earlier work this paper cites.
X. Tao, H. Gao, X. Shen, J. Wang, and J. Jia, “Scale-recurrent network for deep image deblurring,” in CVPR , 2018, pp. 8174–8182
2018
Earlier work this paper cites.
A. Abdelhamed, S. Lin, and M. S. Brown, “A high-quality denoising dataset for smartphone cameras,” in CVPR , 2018, pp. 1692–1700
2018
Earlier work this paper cites.
Y. Qu, Y. Chen, J. Huang, and Y. Xie, “Enhanced pix2pix dehazing network,” in CVPR , 2019, pp. 8160–8168
2019
Earlier work this paper cites.
Z. Jin, M. Z. Iqbal, D. Bobkov, W. Zou, X. Li, and E. Steinbach, “A flexible deep cnn framework for image restoration,” IEEE Transactions on Multimedia , vol. 22, no. 4, pp. 1055–1068, 2019
2019
Earlier work this paper cites.
A. Ignatov and R. Timofte, “Ntire 2019 challenge on image enhancement: Methods and results,” in CVPR Workshops , 2019, pp. 0–0
2019
Earlier work this paper cites.
H. Hu, Z. Zhang, Z. Xie, and S. Lin, “Local relation networks for image recognition,” in ICCV , 2019, pp. 3464–3473
2019
Earlier work this paper cites.
A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan et al. , “Searching for mobilenetv3,” in ICCV , 2019, pp. 1314–1324
2019
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,” TIP , vol. 28, no. 1, pp. 492–505, 2019
2019
Earlier work this paper cites.
C. O. Ancuti, C. Ancuti, M. Sbert, and R. Timofte, “Dense-haze: A benchmark for image dehazing with dense-haze and haze-free images,” in ICIP . IEEE, 2019, pp. 1014–1018
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.
W. Wei, D. Meng, Q. Zhao, Z. Xu, and Y. Wu, “Semi-supervised transfer learning for image rain removal,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 3877–3886
2019
Earlier work this paper cites.
R. Yasarla and V. M. Patel, “Uncertainty guided multi-scale residual learning-using a cycle spinning cnn for single image de-raining,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 8405–8414
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.
Z. Shen, W. Wang, X. Lu, J. Shen, H. Ling, T. Xu, and L. Shao, “Human-aware motion deblurring,” in ICCV , 2019, pp. 5572–5581
2019
Earlier work this paper cites.
O. Kupyn, T. Martyniuk, J. Wu, and Z. Wang, “Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better,” in ICCV , 2019, pp. 8878–8887
2019
Earlier work this paper cites.
H. Zhang, Y. Dai, H. Li, and P. Koniusz, “Deep stacked hierarchical multi-patch network for image deblurring,” in CVPR , 2019, pp. 5978–5986
2019
Cited alongside, same era.
Z. Yue, H. Yong, Q. Zhao, D. Meng, and L. Zhang, “Variational denoising network: Toward blind noise modeling and removal,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
X. Qin, Z. Wang, Y. Bai, X. Xie, and H. Jia, “Ffa-net: Feature fusion attention network for single image dehazing,” in AAAI , vol. 34, no. 07, 2020, pp. 11 908–11 915
2020
Cited alongside, same era.
A. Katharopoulos, A. Vyas, N. Pappas, and F. Fleuret, “Transformers are rnns: Fast autoregressive transformers with linear attention,” in ICML . PMLR, 2020, pp. 5156–5165
2020
Cited alongside, same era.
K. Purohit, M. Suin, A. Rajagopalan, and V. N. Boddeti, “Spatially-adaptive image restoration using distortion-guided networks,” in ICCV , 2021, pp. 2309–2319
2021
Later among the works it cites.
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
Later among the works it cites.
C. O. Ancuti, C. Ancuti, F.-A. Vasluianu, and R. Timofte, “Ntire 2021 nonhomogeneous dehazing challenge report,” in CVPR , 2021, pp. 627–646
2021
Later among the works it cites.
C. Ren, X. He, C. Wang, and Z. Zhao, “Adaptive consistency prior based deep network for image denoising,” in CVPR , 2021, pp. 8596–8606
2021
Later among the works it cites.
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2020
Cited alongside, same era.
S. Anwar, S. Khan, and N. Barnes, “A deep journey into super-resolution: A survey,” ACM Computing Surveys (CSUR) , vol. 53, no. 3, pp. 1–34, 2020
2020
Cited alongside, same era.
C. Tian, L. Fei, W. Zheng, Y. Xu, W. Zuo, and C.-W. Lin, “Deep learning on image denoising: An overview,” Neural Networks , vol. 131, pp. 251–275, 2020
2020
Cited alongside, same era.
X. Zhang, T. Wang, J. Wang, G. Tang, and L. Zhao, “Pyramid channel-based feature attention network for image dehazing,” Computer Vision and Image Understanding , vol. 197, p. 103003, 2020
2020
Cited alongside, same era.
S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, M.-H. Yang, and L. Shao, “Learning enriched features for real image restoration and enhancement,” in ECCV . Springer, 2020, pp. 492–511
2020
Cited alongside, same era.
J. Liu, H. Wu, Y. Xie, Y. Qu, and L. Ma, “Trident dehazing network,” in CVPR Workshops , 2020, pp. 430–431
2020
Cited alongside, same era.
J. Dong and J. Pan, “Physics-based feature dehazing networks,” in ECCV . Springer, 2020, pp. 188–204
2020
Cited alongside, same era.
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 CVPR , 2020, pp. 2157–2167
2020
Cited alongside, same era.
C. Mou, J. Zhang, and Z. Wu, “Dynamic attentive graph learning for image restoration,” in ICCV , 2021, pp. 4328–4337
2021
Later among the works it cites.
S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M.-H. Yang, “Restormer: Efficient transformer for high-resolution image restoration,” in CVPR , 2022, pp. 5728–5739
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 CVPR , 2022, pp. 17 683–17 693
2022
Later among the works it cites.
Y. Lee, J. Kim, J. Willette, and S. J. Hwang, “Mpvit: Multi-path vision transformer for dense prediction,” in CVPR , 2022, pp. 7287–7296
2022
Later among the works it cites.
Z. Tu, H. Talebi, H. Zhang, F. Yang, P. Milanfar, A. Bovik, and Y. Li, “Maxvit: Multi-axis vision transformer,” in ECCV . Springer, 2022, pp. 459–479
2022
Later among the works it cites.
Z. Qin, W. Sun, H. Deng, D. Li, Y. Wei, B. Lv, J. Yan, L. Kong, and Y. Zhong, “cosformer: Rethinking softmax in attention,” ICLR , 2022
2022
Later among the works it cites.
C. Si, W. Yu, P. Zhou, Y. Zhou, X. Wang, and S. Yan, “Inception transformer,” NIPS , vol. 35, pp. 23 495–23 509, 2022
2022
Later among the works it cites.
Y. Yang, H. Zhang, X. Wu, and X. Liang, “Mstfdn: Multi-scale transformer fusion dehazing network,” Applied Intelligence , pp. 1–12, 2022
2022
Later among the works it cites.
Z. Tu, H. Talebi, H. Zhang, F. Yang, P. Milanfar, A. Bovik, and Y. Li, “Maxim: Multi-axis mlp for image processing,” in CVPR , 2022, pp. 5769–5780
2022
Later among the works it cites.
H. Bai, J. Pan, X. Xiang, and J. Tang, “Self-guided image dehazing using progressive feature fusion,” TIP , vol. 31, pp. 1217 – 1229, 2022
2022
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 CVPR , 2022, pp. 5812–5820
2022
Later among the works it cites.
T. Ye, Y. Zhang, M. Jiang, L. Chen, Y. Liu, S. Chen, and E. Chen, “Perceiving and modeling density for image dehazing,” in European conference on computer vision . Springer, 2022, pp. 130–145
2022
Later among the works it cites.
L. Chen, X. Chu, X. Zhang, and J. Sun, “Simple baselines for image restoration,” in ECCV . Springer, 2022, pp. 17–33
2022
Later among the works it cites.
W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pvt v2: Improved baselines with pyramid vision transformer,” Computational Visual Media , vol. 8, no. 3, pp. 415–424, 2022
2022
Later among the works it cites.
X. Dong, J. Bao, D. Chen, W. Zhang, N. Yu, L. Yuan, D. Chen, and B. Guo, “Cswin transformer: A general vision transformer backbone with cross-shaped windows,” in CVPR , 2022, pp. 12 124–12 134
2022
Later among the works it cites.
Y. Qiu, K. Zhang, C. Wang, W. Luo, H. Li, and Z. Jin, “Mb-taylorformer: Multi-branch efficient transformer expanded by taylor formula for image dehazing,” in ICCV , 2023, pp. 12 802–12 813
2023
Later among the works it cites.
D. Han, X. Pan, Y. Han, S. Song, and G. Huang, “Flatten transformer: Vision transformer using focused linear attention,” in ICCV , 2023, pp. 5961–5971
2023
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,” IJCV , 2023
2023
Later among the works it cites.
A. Kulkarni and S. Murala, “Aerial image dehazing with attentive deformable transformers,” in WACV , 2023, pp. 6305–6314
2023
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 CVPR , 2023, pp. 5785–5794
2023
Later among the works it cites.
M.-T. Duong, S. Lee, and M.-C. Hong, “Dmt-net: deep multiple networks for low-light image enhancement based on retinex model,” IEEE Access , vol. 11, pp. 132 147–132 161, 2023
2023
Later among the works it cites.
Y. Cui, W. Ren, X. Cao, and A. Knoll, “Image restoration via frequency selection,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Later among the works it cites.
B. Cheng, J. Li, Y. Chen, and T. Zeng, “Snow mask guided adaptive residual network for image snow removal,” Computer Vision and Image Understanding , vol. 236, p. 103819, 2023
2023
Later among the works it cites.
S. Chen, T. Ye, Y. Liu, T. Liao, J. Jiang, E. Chen, and P. Chen, “Msp-former: Multi-scale projection transformer for single image desnowing,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
Later among the works it cites.
Y. Cui, W. Ren, X. Cao, and A. Knoll, “Focal network for image restoration,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 13 001–13 011
2023
Later among the works it cites.
Y. Cui, W. Ren, S. Yang, X. Cao, and A. Knoll, “Irnext: Rethinking convolutional network design for image restoration,” in International conference on machine learning , 2023
2023
Later among the works it cites.
B. Xia, Y. Zhang, S. Wang, Y. Wang, X. Wu, Y. Tian, W. Yang, and L. Van Gool, “Diffir: Efficient diffusion model for image restoration,” in ICCV , 2023, pp. 13 095–13 105
2023
Later among the works it cites.
S. Dutta, A. Basarab, B. Georgeot, and D. Kouamé, “Diva: Deep unfolded network from quantum interactive patches for image restoration,” Pattern Recognition , p. 110676, 2024
2024
Later among the works it cites.
T. Wang, G. Tao, W. Lu, K. Zhang, W. Luo, X. Zhang, and T. Lu, “Restoring vision in hazy weather with hierarchical contrastive learning,” Pattern Recognition , vol. 145, p. 109956, 2024
2024
Later among the works it cites.
Y. Cui, W. Ren, X. Cao, and A. Knoll, “Revitalizing convolutional network for image restoration,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
2024
Later among the works it cites.
P. Shyam, K.-S. Kim, and K.-J. Yoon, “Giqe: Generic image quality enhancement via nth order iterative degradation,” in CVPR , 2022, pp. 2077–2087
2087
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