Fetching the paper…
Reading the bibliography…
Transformer-based methods have shown impressive performance in image restoration tasks, such as image super-resolution and denoising.
X. Wang, L. Xie, C. Dong, and Y. Shan, “Real-esrgan: Training real-world blind super-resolution with pure synthetic data,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV) , 2021, pp. 1905–1914
1914
Earlier work this paper cites.
G. H. Granlund, “In search of a general picture processing operator,” Comput. Graph. Image Process. , vol. 8, no. 2, pp. 155–173, 1978
1978
Earlier work this paper cites.
S. Yitzhaki, “Relative deprivation and the gini coefficient,” Q. J. Econ. , pp. 321–324, 1979
1979
Earlier work this paper cites.
R. Franzen, “Kodak lossless true color image suite,” http://r0k.us/graphics/kodak , vol. 4, no. 2, 1999
1999
Earlier work this paper cites.
D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV) , vol. 2, 2001, pp. 416–423
2001
Earlier work this paper cites.
H. Sheikh, “Live image quality assessment database release 2,” http://live.ece.utexas.edu/research/quality , 2005
2005
Earlier work this paper cites.
A. Foi, V. Katkovnik, and K. Egiazarian, “Pointwise shape-adaptive dct for high-quality denoising and deblocking of grayscale and color images,” IEEE Trans. Image Process. , vol. 16, no. 5, pp. 1395–1411, 2007
2007
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2009, pp. 248–255
2009
Earlier work this paper cites.
R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse-representations,” in Int. Conf. Curves Surfaces , 2010, pp. 711–730
2010
Earlier work this paper cites.
W. W. Zou and P. C. Yuen, “Very low resolution face recognition problem,” IEEE Trans. Image Process. , vol. 21, no. 1, pp. 327–340, 2011
2011
Earlier work this paper cites.
L. Zhang, X. Wu, A. Buades, and X. Li, “Color demosaicking by local directional interpolation and nonlocal adaptive thresholding,” J. Electron. Imaging , vol. 20, no. 2, pp. 023 016–023 016, 2011
2011
Earlier work this paper cites.
M. Bevilacqua, A. Roumy, C. Guillemot, and M.-L. A. Morel, “Low-complexity single-image super-resolution based on nonnegative neighbor embedding,” in Br. Mach. Vis. Conf. (BMVC) , 2012
2012
Earlier work this paper cites.
W. Shi, J. Caballero, C. Ledig, X. Zhuang, W. Bai, K. Bhatia, A. M. S. M. de Marvao, T. Dawes, D. O’Regan, and D. Rueckert, “Cardiac image super-resolution with global correspondence using multi-atlas patchmatch,” in Med. Image Comput. Comput.-Assist. Intervent. (MICCAI) , 2013, pp. 9–16
2013
Earlier work this paper cites.
C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a deep convolutional network for image super-resolution,” in Eur. Conf. Comput. Vis. (ECCV) , 2014, pp. 184–199
2014
Earlier work this paper cites.
C. Dong, Y. Deng, C. C. Loy, and X. Tang, “Compression artifacts reduction by a deep convolutional network,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV) , 2015, pp. 576–584
2015
Earlier work this paper cites.
C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, no. 2, pp. 295–307, 2015
2015
Earlier work this paper cites.
J.-B. Huang, A. Singh, and N. Ahuja, “Single image super-resolution from transformed self-exemplars,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2015, pp. 5197–5206
2015
Earlier work this paper cites.
C. Dong, C. C. Loy, and X. Tang, “Accelerating the super-resolution convolutional neural network,” in Eur. Conf. Comput. Vis. (ECCV) , 2016, pp. 391–407
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 Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2016, pp. 1874–1883
2016
Earlier work this paper cites.
J. Kim, J. K. Lee, and K. M. Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2016, pp. 1646–1654
2016
Earlier work this paper cites.
J. Kim, J. K. Lee, and K. M. Lee, “Deeply-recursive convolutional network for image super-resolution,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2016, pp. 1637–1645
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. Ma, Z. Duanmu, Q. Wu, Z. Wang, H. Yong, H. Li, and L. Zhang, “Waterloo exploration database: New challenges for image quality assessment models,” IEEE Trans. Image Process. , vol. 26, no. 2, pp. 1004–1016, 2016
2016
Earlier work this paper cites.
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Trans. Image Process. , vol. 26, no. 7, pp. 3142–3155, 2017
2017
Earlier work this paper cites.
K. Zhang, W. Zuo, S. Gu, and L. Zhang, “Learning deep cnn denoiser prior for image restoration,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2017, pp. 3929–3938
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 Inf. Process. Syst. (NeurIPS) , vol. 30, 2017
2017
Earlier work this paper cites.
B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee, “Enhanced deep residual networks for single image super-resolution,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) Workshops , 2017, pp. 136–144
2017
Earlier work this paper cites.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang et al. , “Photo-realistic single image super-resolution using a generative adversarial network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2017, pp. 4681–4690
2017
Earlier work this paper cites.
Y. Tai, J. Yang, and X. Liu, “Image super-resolution via deep recursive residual network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2017, pp. 3147–3155
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 Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2017, pp. 3883–3891
2017
Earlier work this paper cites.
M. Sundararajan, A. Taly, and Q. Yan, “Axiomatic attribution for deep networks,” in Int. Conf. Mach. Learn. (ICML) , 2017, pp. 3319–3328
2017
Earlier work this paper cites.
B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee, “Enhanced deep residual networks for single image super-resolution,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) Workshops , 2017, pp. 136–144
2017
Earlier work this paper cites.
R. Timofte, E. Agustsson, L. Van Gool, M.-H. Yang, and L. Zhang, “Ntire 2017 challenge on single image super-resolution: Methods and results,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) Workshops , 2017, pp. 114–125
2017
Earlier work this paper cites.
Y. Matsui, K. Ito, Y. Aramaki, A. Fujimoto, T. Ogawa, T. Yamasaki, and K. Aizawa, “Sketch-based manga retrieval using manga109 dataset,” Multimedia Tools Appl. , vol. 76, no. 20, pp. 21 811–21 838, 2017
2017
Earlier work this paper cites.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” in Int. Conf. Learn. Represent. (ICLR) , 2018
2018
Earlier work this paper cites.
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “Image super-resolution using very deep residual channel attention networks,” in Eur. Conf. Comput. Vis. (ECCV) , 2018, pp. 286–301
2018
Earlier work this paper cites.
Y. Zhang, K. Li, K. Li, B. Zhong, and Y. Fu, “Residual non-local attention networks for image restoration,” in Int. Conf. Learn. Represent. (ICLR) , 2018
2018
Cited alongside, same era.
X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C. Change Loy, “Esrgan: Enhanced super-resolution generative adversarial networks,” in Eur. Conf. Comput. Vis. (ECCV) Workshops , 2018, pp. 0–0
2018
Cited alongside, same era.
Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, “Residual dense network for image super-resolution,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2018, pp. 2472–2481
2018
Cited alongside, same era.
D. Liu, B. Wen, Y. Fan, C. C. Loy, and T. S. Huang, “Non-local recurrent network for image restoration,” Adv. Neural Inf. Process. Syst. (NeurIPS) , vol. 31, 2018
2018
Cited alongside, same era.
K. Yuan, S. Guo, Z. Liu, A. Zhou, F. Yu, and W. Wu, “Incorporating convolution designs into visual transformers,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV) , 2021, pp. 579–588
2021
Later among the works it cites.
A. Vaswani, P. Ramachandran, A. Srinivas, N. Parmar, B. Hechtman, and J. Shlens, “Scaling local self-attention for parameter efficient visual backbones,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2021, pp. 12 894–12 904
2021
Later among the works it cites.
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou, “Training data-efficient image transformers & distillation through attention,” in Int. Conf. Mach. Learn. (ICML) , 2021, pp. 10 347–10 357
2021
Later among the works it cites.
M. Raghu, T. Unterthiner, S. Kornblith, C. Zhang, and A. Dosovitskiy, “Do vision transformers see like convolutional neural networks?” Adv. Neural Inf. Process. Syst. (NeurIPS) , vol. 34, 2021
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…
K. Zhang, W. Zuo, and L. Zhang, “Ffdnet: Toward a fast and flexible solution for cnn-based image denoising,” IEEE Trans. Image Process. , vol. 27, no. 9, pp. 4608–4622, 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 Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2018, pp. 586–595
2018
Cited alongside, same era.
P. Liu, H. Zhang, K. Zhang, L. Lin, and W. Zuo, “Multi-level wavelet-cnn for image restoration,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) Workshops , 2018, pp. 773–782
2018
Cited alongside, same era.
A. Abdelhamed, S. Lin, and M. S. Brown, “A high-quality denoising dataset for smartphone cameras,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2018, pp. 1692–1700
2018
Cited alongside, same era.
T. Dai, J. Cai, Y. Zhang, S.-T. Xia, and L. Zhang, “Second-order attention network for single image super-resolution,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2019, pp. 11 065–11 074
2019
Cited alongside, same era.
W. Zhang, Y. Liu, C. Dong, and Y. Qiao, “Ranksrgan: Generative adversarial networks with ranker for image super-resolution,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV) , 2019, pp. 3096–3105
2019
Cited alongside, same era.
P. Ramachandran, N. Parmar, A. Vaswani, I. Bello, A. Levskaya, and J. Shlens, “Stand-alone self-attention in vision models,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
J. Cai, H. Zeng, H. Yong, Z. Cao, and L. Zhang, “Toward real-world single image super-resolution: A new benchmark and a new model,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV) , 2019, pp. 3086–3095
2019
Cited alongside, same era.
T. Xiao, P. Dollar, M. Singh, E. Mintun, T. Darrell, and R. Girshick, “Early convolutions help transformers see better,” Adv. Neural Inf. Process. Syst. (NeurIPS) , vol. 34, 2021
2021
Later among the works it cites.
Y. Yuan, R. Fu, L. Huang, W. Lin, C. Zhang, X. Chen, and J. Wang, “Hrformer: High-resolution vision transformer for dense predict,” Adv. Neural Inf. Process. Syst. (NeurIPS) , vol. 34, pp. 7281–7293, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, M.-H. Yang, and L. Shao, “Multi-stage progressive image restoration,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2021, pp. 14 821–14 831
2021
Later among the works it cites.
2021
Later among the works it cites.
H. Bao, L. Dong, S. Piao, and F. Wei, “Beit: Bert pre-training of image transformers,” in Int. Conf. Learn. Represent. (ICLR) , 2021
2021
Later among the works it cites.
K. Zhang, J. Liang, L. Van Gool, and R. Timofte, “Designing a practical degradation model for deep blind image super-resolution,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV) , 2021, pp. 4791–4800
2021
Later among the works it cites.
J. Jiang, K. Zhang, and R. Timofte, “Towards flexible blind jpeg artifacts removal,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV) , 2021, pp. 4997–5006
2021
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 Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 17 683–17 693
2022
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 Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 5728–5739
2022
Later among the works it cites.
C. Chen, X. Shi, Y. Qin, X. Li, X. Han, T. Yang, and S. Guo, “Real-world blind super-resolution via feature matching with implicit high-resolution priors,” in Proc. ACM Int. Conf. Multimedia , 2022, pp. 1329–1338
2022
Later among the works it cites.
X. Kong, X. Liu, J. Gu, Y. Qiao, and C. Dong, “Reflash dropout in image super-resolution,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 6002–6012
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 Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 12 124–12 134
2022
Later among the works it cites.
S. Wu, T. Wu, H. Tan, and G. Guo, “Pale transformer: A general vision transformer backbone with pale-shaped attention,” in Proc. AAAI Conf. Artif. Intell. , vol. 36, no. 3, 2022, pp. 2731–2739
2022
Later among the works it cites.
K. Patel, A. M. Bur, F. Li, and G. Wang, “Aggregating global features into local vision transformer,” in Proc. Int. Conf. Pattern Recognit. (ICPR) . IEEE, 2022, pp. 1141–1147
2022
Later among the works it cites.
G. Huang, Y. Wang, K. Lv, H. Jiang, W. Huang, P. Qi, and S. Song, “Glance and focus networks for dynamic visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 45, no. 4, pp. 4605–4621, 2022
2022
Later among the works it cites.
H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang, “Swin-unet: Unet-like pure transformer for medical image segmentation,” in Eur. Conf. Comput. Vis. (ECCV) , 2022, pp. 205–218
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 Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 5769–5780
2022
Later among the works it cites.
L. Chen, X. Chu, X. Zhang, and J. Sun, “Simple baselines for image restoration,” in Eur. Conf. Comput. Vis. (ECCV) , 2022, pp. 17–33
2022
Later among the works it cites.
L. Liu, L. Xie, X. Zhang, S. Yuan, X. Chen, W. Zhou, H. Li, and Q. Tian, “Tape: Task-agnostic prior embedding for image restoration,” in Eur. Conf. Comput. Vis. (ECCV) , 2022, pp. 447–464
2022
Later among the works it cites.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 16 000–16 009
2022
Later among the works it cites.
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. (ECCV) , 2022, pp. 574–591
2022
Later among the works it cites.
W. Li, X. Lu, S. Qian, and J. Lu, “On efficient transformer-based image pre-training for low-level vision,” in Proc. Int. Joint Conf. Artif. Intell. , 2023, pp. 1089–1097
2023
Closest in time.
X. Chen, X. Wang, J. Zhou, Y. Qiao, and C. Dong, “Activating more pixels in image super-resolution transformer,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 22 367–22 377
2023
Closest in time.
Y. Liu, H. Zhao, J. Gu, Y. Qiao, and C. Dong, “Evaluating the generalization ability of super-resolution networks,” IEEE Trans. Pattern Anal. Mach. Intell. , 2023
2023
Closest in time.
K. Li, Y. Wang, J. Zhang, P. Gao, G. Song, Y. Liu, H. Li, and Y. Qiao, “Uniformer: Unifying convolution and self-attention for visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell. , 2023
2023
Closest in time.
K. Zhang, Y. Li, J. Liang, J. Cao, Y. Zhang, H. Tang, D.-P. Fan, R. Timofte, and L. V. Gool, “Practical blind image denoising via swin-conv-unet and data synthesis,” Mach. Intell. Res. , vol. 20, no. 6, pp. 822–836, 2023
2023
Closest in time.
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 Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 18 278–18 289
2023
Closest in time.
J. Liang, J. Cao, Y. Fan, K. Zhang, R. Ranjan, Y. Li, R. Timofte, and L. Van Gool, “Vrt: A video restoration transformer,” IEEE Trans. Image Process. , 2024
2024
Closest in time.
J. Hu, J. Gu, S. Yu, F. Yu, Z. Li, Z. You, C. Lu, and C. Dong, “Interpreting low-level vision models with causal effect maps,” IEEE Trans. Pattern Anal. Mach. Intell. , 2025
2025
Closest in time.