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Image restoration involves recovering a high-quality clean image from its degraded version.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
Martin, D., C. Fowlkes, D. Tal, and J. Malik (2001) · 2001
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Language models are few-shot learners
Brown, T. B., B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al. (2020) · 2005
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Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminance-chrominance space
Dabov, K., A. Foi, V. Katkovnik, and K. Egiazarian (2007) · 2007
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Deep photo: Model-based photograph enhancement and viewing
Kopf, J., B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski (2008) · 2008
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al. (2020) · 2010
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Single image haze removal using dark channel prior
He, K., J. Sun, and X. Tang (2010) · 2010
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Single-image super-resolution using sparse regression and natural image prior
Kim, K. I. and Y. Kwon (2010) · 2010
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Deformable detr: Deformable transformers for end-to-end object detection
Zhu, X., W. Su, L. Lu, B. Li, X. Wang, and J. Dai (2020) · 2010
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Contour detection and hierarchical image segmentation
Arbelaez, P., M. Maire, C. Fowlkes, and J. Malik (2011) · 2011
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Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization
Dong, W., L. Zhang, G. Shi, and X. Wu (2011) · 2011
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Nonparametric blind super-resolution
Michaeli, T. and M. Irani (2013) · 2013
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Anchored neighborhood regression for fast example-based super-resolution
Timofte, R., V. De Smet, and L. Van Gool (2013) · 2013
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Single image super-resolution from transformed self-exemplars
Huang, J.-B., A. Singh, and N. Ahuja (2015) · 2015
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Ba, J. L., J. R. Kiros, and G. E. Hinton (2016) · 2016
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Dehazenet: An end-to-end system for single image haze removal
Cai, B., X. Xu, K. Jia, C. Qing, and D. Tao (2016) · 2016
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Waterloo exploration database: New challenges for image quality assessment models
Ma, K., Z. Duanmu, Q. Wu, Z. Wang, H. Yong, H. Li, and L. Zhang (2016) · 2016
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Single image dehazing via multi-scale convolutional neural networks
Ren, W., S. Liu, H. Zhang, J. Pan, X. Cao, and M.-H. Yang (2016) · 2016
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Aod-net: All-in-one dehazing network
Li, B., X. Peng, Z. Wang, J. Xu, and D. Feng (2017) · 2017
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Attention is all you need
Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin (2017) · 2017
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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
Zhang, K., W. Zuo, Y. Chen, D. Meng, and L. Zhang (2017) · 2017
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Learning deep CNN denoiser prior for image restoration
Zhang, K., W. Zuo, S. Gu, and L. Zhang (2017) · 2017
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Benchmarking single-image dehazing and beyond
Li, B., W. Ren, D. Fu, D. Tao, D. Feng, W. Zeng, and Z. Wang (2018) · 2018
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Density-aware single image de-raining using a multi-stream dense network
Zhang, H. and V. M. Patel (2018) · 2018
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Ffdnet: Toward a fast and flexible solution for cnn-based image denoising
Zhang, K., W. Zuo, and L. Zhang (2018) · 2018
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Blind image super-resolution with spatially variant degradations
Cornillere, V., A. Djelouah, W. Yifan, O. Sorkine-Hornung, and C. Schroers (2019) · 2019
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A general decoupled learning framework for parameterized image operators
Fan, Q., D. Chen, L. Yuan, G. Hua, N. Yu, and B. Chen (2019) · 2019
Cited alongside, same era.
Dynamic scene deblurring with parameter selective sharing and nested skip connections
Gao, H., X. Tao, X. Shen, and J. Jia (2019) · 2019
Cited alongside, same era.
Parameter-efficient transfer learning for nlp
Houlsby, N., A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly (2019) · 2019
Cited alongside, same era.
Enhanced pix2pix dehazing network
Qu, Y., Y. Chen, J. Huang, and Y. Xie (2019) · 2019
Cited alongside, same era.
Semi-supervised transfer learning for image rain removal
Wei, W., D. Meng, Q. Zhao, Z. Xu, and Y. Wu (2019) · 2019
Cited alongside, same era.
Uncertainty guided multi-scale residual learning-using a cycle spinning cnn for single image de-raining
Tokens-to-token vit: Training vision transformers from scratch on imagenet
Yuan, L., Y. Chen, T. Wang, W. Yu, Y. Shi, Z. Jiang, F. E. Tay, J. Feng, and S. Yan (2021) · 2021
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Multi-stage progressive image restoration
Zamir, S. W., A. Arora, S. Khan, M. Hayat, F. S. Khan, M.-H. Yang, and L. Shao (2021) · 2021
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Designing a practical degradation model for deep blind image super-resolution
Zhang, K., J. Liang, L. Van Gool, and R. Timofte (2021) · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Zheng, S., J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P. H. Torr, et al. (2021) · 2021
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Simple baselines for image restoration
Chen, L., X. Chu, X. Zhang, and J. Sun (2022) · 2022
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Yasarla, R. and V. M. Patel (2019) · 2019
Cited alongside, same era.
End-to-end object detection with transformers
Carion, N., F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko (2020) · 2020
Cited alongside, same era.
Multi-scale boosted dehazing network with dense feature fusion
Dong, H., J. Pan, L. Xiang, Z. Hu, X. Zhang, F. Wang, and M.-H. Yang (2020) · 2020
Cited alongside, same era.
Fd-gan: Generative adversarial networks with fusion-discriminator for single image dehazing
Dong, Y., Y. Liu, H. Zhang, S. Chen, and Y. Qiao (2020) · 2020
Cited alongside, same era.
Multi-scale progressive fusion network for single image deraining
Jiang, K., Z. Wang, P. Yi, C. Chen, B. Huang, Y. Luo, J. Ma, and J. Jiang (2020) · 2020
Cited alongside, same era.
All in one bad weather removal using architectural search
Li, R., R. T. Tan, and L.-F. Cheong (2020) · 2020
Cited alongside, same era.
Trident dehazing network
Liu, J., H. Wu, Y. Xie, Y. Qu, and L. Ma (2020) · 2020
Cited alongside, same era.
Chen, X., X. Wang, J. Zhou, and C. Dong · 2022
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Visual prompt tuning for test-time domain adaptation
Gao, Y., X. Shi, Y. Zhu, H. Wang, Z. Tang, X. Zhou, M. Li, and D. N. Metaxas (2022) · 2022
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Hyperprompt: Prompt-based task-conditioning of transformers
He, Y., S. Zheng, Y. Tay, J. Gupta, Y. Du, V. Aribandi, Z. Zhao, Y. Li, Z. Chen, D. Metzler, et al. (2022) · 2022
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Visual prompt tuning
Jia, M., L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim (2022) · 2022
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Transformers in vision: A survey
Khan, S., M. Naseer, M. Hayat, S. W. Zamir, F. S. Khan, and M. Shah (2022) · 2022
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All-in-one image restoration for unknown corruption
Li, B., X. Liu, P. Hu, Z. Wu, J. Lv, and X. Peng (2022, June) · 2022
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Tape: Task-agnostic prior embedding for image restoration
Liu, L., L. Xie, X. Zhang, S. Yuan, X. Chen, W. Zhou, H. Li, and Q. Tian (2022) · 2022
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Learning the degradation distribution for blind image super-resolution
Luo, Z., Y. Huang, S. Li, L. Wang, and T. Tan (2022) · 2022
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Clean images are hard to reblur: Exploiting the ill-posed inverse task for dynamic scene deblurring
Nah, S., S. Son, J. Lee, and K. M. Lee (2022) · 2022
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Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning
Smith, J. S., L. Karlinsky, V. Gutta, P. Cascante-Bonilla, D. Kim, A. Arbelle, R. Panda, R. Feris, and Z. Kira (2022) · 2022
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Visual prompt tuning for generative transfer learning
Sohn, K., Y. Hao, J. Lezama, L. Polania, H. Chang, H. Zhang, I. Essa, and L. Jiang (2022) · 2022
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BANet: A blur-aware attention network for dynamic scene deblurring
Tsai, F.-J., Y.-T. Peng, C.-C. Tsai, Y.-Y. Lin, and C.-W. Lin (2022) · 2022
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MAXIM: Multi-axis MLP for image processing
Tu, Z., H. Talebi, H. Zhang, F. Yang, P. Milanfar, A. Bovik, and Y. Li (2022) · 2022
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Transweather: Transformer-based restoration of images degraded by adverse weather conditions
Valanarasu, J. M. J., R. Yasarla, and V. M. Patel (2022) · 2022
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Dualprompt: Complementary prompting for rehearsal-free continual learning
Wang, Z., Z. Zhang, S. Ebrahimi, R. Sun, H. Zhang, C.-Y. Lee, X. Ren, G. Su, V. Perot, J. Dy, et al. (2022) · 2022
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Learning to prompt for continual learning
Wang, Z., Z. Zhang, C.-Y. Lee, H. Zhang, R. Sun, X. Ren, G. Su, V. Perot, J. Dy, and T. Pfister (2022) · 2022
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Restormer: Efficient transformer for high-resolution image restoration
Zamir, S. W., A. Arora, S. Khan, M. Hayat, F. S. Khan, and M.-H. Yang (2022) · 2022
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Learning to prompt for vision-language models
Zhou, K., J. Yang, C. C. Loy, and Z. Liu (2022) · 2022
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Maple: Multi-modal prompt learning
Khattak, M. U., H. Rasheed, M. Maaz, S. Khan, and F. S. Khan (2023) · 2023
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Multitask prompt tuning enables parameter-efficient transfer learning
Wang, Z., R. Panda, L. Karlinsky, R. Feris, H. Sun, and Y. Kim (2023) · 2023
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