R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, W. T. Freeman, Removing camera shake from a single photograph. ACM TOG (Proc. SIGGRAPH), 787–794 (2006).
Q. Shan, J. Jia, A. Agarwala, High-quality motion deblurring from a single image. ACM TOG (Proc. SIGGRAPH). 27(3), 73–17310 (2008).
Google Scholar
A. Levin, Y. Weiss, F. Durand, W. T. Freeman, in Efficient marginal likelihood optimization in blind deconvolution. Conference on Computer Vision and Pattern Recognition (IEEE, 2011).
L. Sun, S. Cho, J. Wang, J. Hays, in Edge-based blur kernel estimation using patch priors. International Conference on Computational Photography (IEEE, 2013).
J. Pan, D. Sun, H. Pfister, M. Yang, in Blind image deblurring using dark channel prior. Conference on Computer Vision and Pattern Recognition (IEEE, 2016).
S. Cho, S. Lee, Fast motion deblurring. ACM TOG (Proc. SIGGRAPH Asia). 28(5), 145–11458 (2009).
Google Scholar
A. Levin, Y. Weiss, F. Durand, W. T. Freeman, in Understanding and evaluating blind deconvolution algorithms. Conference on Computer Vision and Pattern Recognition (IEEE, 2009).
L. Xu, J. S. J. Ren, Q. Yan, R. Liao, J. Jia, in Deep edge-aware filters. International Conference on Machine Learning, (2015), pp. 1669–1678.
C. J. Schuler, H. C. Burger, S. Harmeling, B. Schölkopf, in A machine learning approach for non-blind image deconvolution. Conference on Computer Vision and Pattern Recognition (IEEE, 2013).
L. Xu, J. S. J. Ren, C. Liu, J. Jia, in Deep convolutional neural network for image deconvolution. Neural Information Processing Systems, (2014).
J. Zhang, J. Pan, W. -S. Lai, R. W. H. Lau, M. -H. Yang, in Learning fully convolutional networks for iterative non-blind deconvolution. Conference on Computer Vision and Pattern Recognition (IEEE, 2017).
J. Sun, W. Cao, Z. Xu, J. Ponce, in Learning a convolutional neural network for non-uniform motion blur removal. Conference on Computer Vision and Pattern Recognition (IEEE, 2015).
A. Chakrabarti, in A neural approach to blind motion deblurring. European Conference on Computer Vision, (2016).
C. J. Schuler, M. Hirsch, S. Harmeling, B. Schölkopf, Learning to deblur. TPAMI. 38(7), 1439–1451 (2016).
Article
Google Scholar
S. Nah, T. Hyun Kim, K. Mu Lee, in Deep multi-scale convolutional neural network for dynamic scene deblurring. Conference on Computer Vision and Pattern Recognition (IEEE, 2017).
Z. Shen, W. Lai, T. Xu, J. Kautz, M. Yang, Deep semantic face deblurring, (2018).
D. Krishnan, T. Tay, R. Fergus, in Blind deconvolution using a normalized sparsity measure. Conference on Computer Vision and Pattern Recognition (IEEE, 2011).
L. Xu, S. Zheng, J. Jia, in Unnatural L0 sparse representation for natural image deblurring. Conference on Computer Vision and Pattern Recognition (IEEE, 2013).
T. Michaeli, M. Irani, in Blind deblurring using internal patch recurrence. European Conference on Computer Vision, (2014).
J. Pan, Z. Hu, Z. Su, M. Yang, in Deblurring text images via l0-regularized intensity and gradient prior. Conference on Computer Vision and Pattern Recognition (IEEE, 2014).
R. Yan, L. Shao, Blind image blur estimation via deep learning. TIP. 25(4), 1910–1921 (2016).
MathSciNet
Google Scholar
I. Misra, A. Shrivastava, A. Gupta, M. Hebert, in Cross-stitch networks for multi-task learning. Conference on Computer Vision and Pattern Recognition (IEEE, 2016).
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, Y. LeCun, Overfeat: Integrated recognition, localization and detection using convolutional networks. CoRR (2013). https://doi.org/abs/1312.6229.
Z. Zhang, P. Luo, C. C. Loy, X. Tang, in Facial landmark detection by deep multi-task learning. European Conference on Computer Vision, (2014).
L. Trottier, P. Giguère, B. Chaib-draa, Multi-task learning by deep collaboration and application in facial landmark detection. CoRR (2017). https://doi.org/abs/1711.00111.
J. Johnson, A. Alahi, L. Fei-Fei, in Perceptual losses for real-time style transfer and super-resolution. European Conference on Computer Vision, (2016).
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, W. Shi, in Photo-realistic single image super-resolution using a generative adversarial network. Conference on Computer Vision and Pattern Recognition (IEEE, 2017).
L. Sun, J. Hays, Super-resolution using constrained deep texture synthesis. CoRR (2017). https://doi.org/abs/1701.07604.
L. A. Gatys, A. S. Ecker, M. Bethge, in Texture synthesis using convolutional neural networks. Neural Information Processing Systems, (2015).
K. Simonyan, A. Zisserman, in Very deep convolutional networks for large-scale image recognition. ICLR, (2015).
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, in Generative adversarial nets. Neural Information Processing Systems, (2014).
R. Huang, S. Zhang, T. Li, R. He, in Beyond face rotation: Global and local perception GAN for photorealistic and identity preserving frontal view synthesis. ICCV, (2017).
V. Le, J. Brandt, Z. Lin, L. Bourdev, T. S. Huang, in Interactive facial feature localization. European Conference on Computer Vision, (2012).
T. Sim, S. Baker, M. Bsat, in International Conference on Automatic Face and Gesture Recognition. The cmu pose, illumination, and expression (pie) database (IEEE, 2002).
Z. Liu, P. Luo, X. Wang, X. Tang, in Deep learning face attributes in the wild. International Conference on Computer Vision (IEEE, 2015).
G. Boracchi, A. Foi, Modeling the performance of image restoration from motion blur. TIP. 21(8), 3502–3517 (2012).
MathSciNet
MATH
Google Scholar
J. Pan, Z. Hu, Z. Su, M. Yang, in Deblurring face images with exemplars. European Conference on Computer Vision, (2014).
L. Zhong, S. Cho, D. N. Metaxas, S. Paris, J. Wang, in Handling noise in single image deblurring using directional filters. Conference on Computer Vision and Pattern Recognition (IEEE, 2013).
F. Schroff, D. Kalenichenko, J. Philbin, in FaceNet: A unified embedding for face recognition and clustering. Conference on Computer Vision and Pattern Recognition (IEEE, 2015).