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Under-Display Camera (UDC) is an emerging technology that achieves full-screen display via hiding the camera under the display panel.
J. Koh, J. Lee, and S. Yoon, “Bnudc: A two-branched deep neural network for restoring images from under-display cameras,” in IEEE Conference on Computer Vision and Pattern Recognition , 2022, pp. 1950–1959
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2015
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Z. Qin, Y.-H. Tsai, Y.-W. Yeh, Y.-P. Huang, and H.-P. D. Shieh, “See-through image blurring of transparent organic light-emitting diodes display: calculation method based on diffraction and analysis of pixel structures,” Journal of Display Technology , pp. 1242–1249, 2016
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F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 fourth international conference on 3D vision (3DV) , 2016, pp. 565–571
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J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on Computer Vision , 2016, pp. 694–711
2016
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems , 2017
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in IEEE International Conference on Computer Vision , 2017, pp. 2980–2988
2017
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L. N. Smith, “Cyclical learning rates for training neural networks,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . IEEE, 2017, pp. 464–472
2017
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X. Wang, K. Yu, C. Dong, and C. C. Loy, “Recovering realistic texture in image super-resolution by deep spatial feature transform,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 606–615
2018
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R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 586–595
2018
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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
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2019
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2019
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2019
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V. Sundar, S. Hegde, D. Kothandaraman, and K. Mitra, “Deep atrous guided filter for image restoration in under display cameras,” in European Conference on Computer Vision Workshop , 2020, pp. 379–397
2020
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H. Panikkasseril Sethumadhavan, D. Puthussery, M. Kuriakose, and J. Charangatt Victor, “Transform domain pyramidal dilated convolution networks for restoration of under display camera images,” in European Conference on Computer Vision Workshop , 2020, pp. 364–378
2020
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Y. Zhou, M. Kwan, K. Tolentino, N. Emerton, S. Lim, T. Large, L. Fu, Z. Pan, B. Li, Q. Yang et al. , “Udc 2020 challenge on image restoration of under-display camera: Methods and results,” in European Conference on Computer Vision Workshop , 2020, pp. 337–351
2020
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F. Wen, R. Ying, Y. Liu, P. Liu, and T.-K. Truong, “A simple local minimal intensity prior and an improved algorithm for blind image deblurring,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 8, pp. 2923–2937, 2020
2020
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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 Conference on Computer Vision and Pattern Recognition , 2020, pp. 8346–8355
2020
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K. Jiang, Z. Wang, P. Yi, C. Chen, Z. Han, T. Lu, B. Huang, and J. Jiang, “Decomposition makes better rain removal: An improved attention-guided deraining network,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 10, pp. 3981–3995, 2020
2020
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X. Zhang, T. Wang, W. Luo, and P. Huang, “Multi-level fusion and attention-guided cnn for image dehazing,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 11, pp. 4162–4173, 2020
2020
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2020
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N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European Conference on Computer Vision , 2020, pp. 213–229
2020
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Y. Tay, D. Bahri, L. Yang, D. Metzler, and D.-C. Juan, “Sparse sinkhorn attention,” in International Conference on Machine Learning . PMLR, 2020, pp. 9438–9447
2020
Cited alongside, same era.
K. Ding, K. Ma, S. Wang, and E. P. Simoncelli, “Image quality assessment: Unifying structure and texture similarity,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 5, pp. 2567–2581, 2020
2020
Cited alongside, same era.
Y. Zhou, D. Ren, N. Emerton, S. Lim, and T. Large, “Image restoration for under-display camera,” in IEEE Conference on Computer Vision and Pattern Recognition , 2021, pp. 9179–9188
2021
Cited alongside, same era.
Z. Tu, H. Talebi, H. Zhang, F. Yang, P. Milanfar, A. Bovik, and Y. Li, “Maxim: Multi-axis mlp for image processing,” in IEEE Conference on Computer Vision and Pattern Recognition , 2022, pp. 5769–5780
2022
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K. Zhang, W. Luo, Y. Yu, W. Ren, F. Zhao, C. Li, L. Ma, W. Liu, and H. Li, “Beyond monocular deraining: Parallel stereo deraining network via semantic prior,” International Journal of Computer Vision , vol. 130, no. 7, pp. 1754–1769, 2022
2022
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S. Khan, M. Naseer, M. Hayat, S. W. Zamir, F. S. Khan, and M. Shah, “Transformers in vision: A survey,” ACM computing surveys (CSUR) , pp. 1–41, 2022
2022
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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 Transactions on Pattern Analysis and Machine Intelligence , pp. 87–110, 2022
2022
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R. Feng, C. Li, H. Chen, S. Li, C. C. Loy, and J. Gu, “Removing diffraction image artifacts in under-display camera via dynamic skip connection network,” in IEEE Conference on Computer Vision and Pattern Recognition , 2021, pp. 662–671
2021
Cited alongside, same era.
A. Yang and A. C. Sankaranarayanan, “Designing display pixel layouts for under-panel cameras,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
Cited alongside, same era.
Z. Zhang, “14.4: Diffraction simulation of camera under display,” in SID Symposium Digest of Technical Papers , 2021, pp. 93–96
2021
Cited alongside, same era.
Q. Yang, Y. Liu, J. Tang, and T. Ku, “Residual and dense unet for under-display camera restoration,” in European Conference on Computer Vision Workshop , 2021, pp. 398–408
2021
Cited alongside, same era.
2021
Cited alongside, same era.
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 IEEE Conference on Computer Vision and Pattern Recognition , 2021, pp. 12 299–12 310
2021
Cited alongside, same era.
T. Wang, X. Zhang, R. Jiang, L. Zhao, H. Chen, and W. Luo, “Video deblurring via spatiotemporal pyramid network and adversarial gradient prior,” Computer Vision and Image Understanding , vol. 203, p. 103135, 2021
2021
Cited alongside, same era.
J. Li, X. Feng, and Z. Hua, “Low-light image enhancement via progressive-recursive network,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 11, pp. 4227–4240, 2021
2021
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S. Liu, J. Ye, S. Ren, and X. Wang, “Dynast: Dynamic sparse transformer for exemplar-guided image generation,” in European Conference on Computer Vision , 2022, pp. 72–90
2022
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L. Fan, Z. Pang, T. Zhang, Y.-X. Wang, H. Zhao, F. Wang, N. Wang, and Z. Zhang, “Embracing single stride 3d object detector with sparse transformer,” in IEEE Conference on Computer Vision and Pattern Recognition , 2022, pp. 8458–8468
2022
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2022
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A. Aakerberg, A. S. Johansen, K. Nasrollahi, and T. B. Moeslund, “Semantic segmentation guided real-world super-resolution,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2022, pp. 449–458
2022
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H. Wang, J. Shen, Y. Liu, Y. Gao, and E. Gavves, “Nformer: Robust person re-identification with neighbor transformer,” in IEEE Conference on Computer Vision and Pattern Recognition , 2022, pp. 7297–7307
2022
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P. Wang, X. Wang, F. Wang, M. Lin, S. Chang, H. Li, and R. Jin, “Kvt: k-nn attention for boosting vision transformers,” in European Conference on Computer Vision . Springer, 2022, pp. 285–302
2022
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2022
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C. X. Xu, Q. Yao, W. He, S. Shu, and G. C. Yuan, “P-125: A novel method to increase the transmittance of full display with camera,” in SID Symposium Digest of Technical Papers , 2023, pp. 1316–1318
2023
Later among the works it cites.
Y. Li, J. Wu, and Z. Shi, “Lightweight neural network for enhancing imaging performance of under-display camera,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
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R. Feng, C. Li, S. Zhou, W. Sun, Q. Zhu, J. Jiang, Q. Yang, C. C. Loy, J. Gu, Y. Zhu et al. , “Mipi 2022 challenge on under-display camera image restoration: Methods and results,” in European Conference on Computer Vision Workshop , 2023, pp. 60–77
2023
Later among the works it cites.
X. Liu, J. Hu, X. Chen, and C. Dong, “Udc-unet: Under-display camera image restoration via u-shape dynamic network,” in European Conference on Computer Vision Workshop , 2023, pp. 113–129
2023
Later among the works it cites.
R. Feng, C. Li, H. Chen, S. Li, J. Gu, and C. C. Loy, “Generating aligned pseudo-supervision from non-aligned data for image restoration in under-display camera,” in IEEE Conference on Computer Vision and Pattern Recognition , 2023, pp. 5013–5022
2023
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M. V. Conde, F. Vasluianu, J. Vazquez-Corral, and R. Timofte, “Perceptual image enhancement for smartphone real-time applications,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 1848–1858
2023
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J. Tan, X. Chen, T. Wang, K. Zhang, W. Luo, and X. Cao, “Blind face restoration for under-display camera via dictionary guided transformer,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
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T. Wang, K. Zhang, T. Shen, W. Luo, B. Stenger, and T. Lu, “Ultra-high-definition low-light image enhancement: a benchmark and transformer-based method,” in AAAI Conference on Artificial Intelligence , 2023, pp. 2654–2662
2023
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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 IEEE International Conference on Computer Vision , 2023, pp. 12 802–12 813
2023
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A. Yang, E. Kang, H.-E. Lee, and A. C. Sankaranarayanan, “Designing phase masks for under-display cameras,” in IEEE International Conference on Computer Vision , 2023, pp. 10 637–10 645
2023
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B. Song, X. Chen, S. Xu, and J. Zhou, “Under-display camera image restoration with scattering effect,” in IEEE International Conference on Computer Vision , 2023, pp. 12 580–12 589
2023
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C. Liu, X. Wang, S. Li, Y. Wang, and X. Qian, “Fsi: Frequency and spatial interactive learning for image restoration in under-display cameras,” in IEEE International Conference on Computer Vision , 2023, pp. 12 537–12 546
2023
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X. Chen, H. Li, M. Li, and J. Pan, “Learning a sparse transformer network for effective image deraining,” in IEEE Conference on Computer Vision and Pattern Recognition , 2023, pp. 5896–5905
2023
Later among the works it cites.
S. Chen, T. Ye, J. Bai, E. Chen, J. Shi, and L. Zhu, “Sparse sampling transformer with uncertainty-driven ranking for unified removal of raindrops and rain streaks,” in IEEE International Conference on Computer Vision , 2023, pp. 13 106–13 117
2023
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2023
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K. Kwon, E. Kang, S. Lee, S.-J. Lee, H.-E. Lee, B. Yoo, and J.-J. Han, “Controllable image restoration for under-display camera in smartphones,” in IEEE Conference on Computer Vision and Pattern Recognition , 2021, pp. 2073–2082
2082
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