Fetching the paper…
Reading the bibliography…
Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic splitting method with Richardson fixed-point iterations for its least-squares updates and a proximal operator for the auxiliary variable updates.
Wiener, N.: The Extrapolation, Interpolation, and Smoothing of Stationary Time Series. John Wiley & Sons, Inc. (1949)
1949
Earlier work this paper cites.
Richardson, W.H.: Bayesian-based iterative method of image restoration. Journal of the Optical Society of America 62
1972
Earlier work this paper cites.
Folland, G.B.: Fourier Analysis and its Applications. Wadsworth (1992)
1992
Earlier work this paper cites.
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60
1992
Earlier work this paper cites.
Geman, D., Yang, C.: Nonlinear image recovery with half-quadratic regularization. IEEE Transactions in Image Processing 4
1995
Earlier work this paper cites.
Kelley, T.: Iterative Methods for Linear and Nonlinear Equations. SIAM (1995)
1995
Earlier work this paper cites.
Goodman, J.: Introduction to Fourier optics. McGraw-Hill (1996)
1996
Earlier work this paper cites.
Starck, J., Murtagh, F.: Astronomical Image and Data Analysis, Second Edition. Astronomy and Astrophysics Library, Springer (2006)
2006
Earlier work this paper cites.
Cho, S., Matsushita, Y., Lee, S.: Removing non-uniform motion blur from images. In: Proceedings of the International Conference on Computer Vision. pp. 1–8 (2007)
2007
Earlier work this paper cites.
Foi, A., Trimeche, M., Katkovnik, V., Egiazarian, K.O.: Practical poissonian-gaussian noise modeling and fitting for single-image raw-data. IEEE Transactions in Image Processing 17
2008
Earlier work this paper cites.
Sun, J., Xu, Z., Shum, H.: Image super-resolution using gradient profile prior. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 1–8 (2008)
2008
Earlier work this paper cites.
Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-laplacian priors. In: Advances in Neural Information Processing Systems. pp. 1033–1041 (2009)
2009
Earlier work this paper cites.
Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithms. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 1964–1971 (2009)
2009
Earlier work this paper cites.
Roth, S., Black, M.J.: Fields of experts. International Journal on Computer Vision 82
2009
Earlier work this paper cites.
Chakrabarti, A., Zickler, T.E., Freeman, W.T.: Analyzing spatially-varying blur. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 2512–2519 (2010)
2010
Earlier work this paper cites.
Elad, M.: Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer Publishing Company, Incorporated (2010)
2010
Earlier work this paper cites.
Hirsch, M., Sra, S., Schölkopf, B., Harmeling, S.: Efficient filter flow for space-variant multiframe blind deconvolution. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 607–614 (2010)
2010
Cited alongside, same era.
Tai, Y., Tan, P., Brown, M.S.: Richardson-lucy deblurring for scenes under a projective motion path. IEEE Transactions on Pattern Analysis and Machine Intelligence 33
2011
Cited alongside, same era.
Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: Proceedings of the International Conference on Computer Vision. pp. 479–486 (2011)
2011
Cited alongside, same era.
Whyte, O., Sivic, J., Zisserman, A., Ponce, J.: Non-uniform deblurring for shaken images. International Journal on Computer Vision 98
2012
Cited alongside, same era.
Sun, J., Cao, W., Xu, Z., Ponce, J.: Learning a convolutional neural network for non-uniform motion blur removal. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 769–777 (2015)
2015
Later among the works it cites.
Chakrabarti, A.: A neural approach to blind motion deblurring. In: Proceedings of the European Conference on Computer Vision. pp. 221–235 (2016)
2016
Later among the works it cites.
Hu, Z., Yuan, L., Lin, S., Yang, M.: Image deblurring using smartphone inertial sensors. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 1855–1864 (2016)
2016
Later among the works it cites.
Schuler, C.J., Hirsch, M., Harmeling, S., Schölkopf, B.: Learning to deblur. IEEE Transactions on Pattern Analysis and Machine Intelligence 38
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Couzinie-Devy, F., Sun, J., Alahari, K., Ponce, J.: Learning to estimate and remove non-uniform image blur. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 1075–1082 (2013)
2013
Cited alongside, same era.
Schmidt, U., Rother, C., Nowozin, S., Jancsary, J., Roth, S.: Discriminative non-blind deblurring. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 604–611 (2013)
2013
Cited alongside, same era.
Sun, L., Cho, S., Wang, J., Hays, J.: Edge-based blur kernel estimation using patch priors. In: Proceedings of International Conference on Computational Photography. pp. 1–8 (2013)
2013
Cited alongside, same era.
Xu, L., Zheng, S., Jia, J.: Unnatural L0 sparse representation for natural image deblurring. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 1107–1114 (2013)
2013
Cited alongside, same era.
Boyd, S.P., Vandenberghe, L.: Convex Optimization. Cambridge University Press (2014)
2014
Cited alongside, same era.
Kim, T.H., Lee, K.M.: Segmentation-free dynamic scene deblurring. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 2766–2773 (2014)
2014
Cited alongside, same era.
Michaeli, T., Irani, M.: Blind deblurring using internal patch recurrence. In: Proceedings of the European Conference on Computer Vision. pp. 783–798 (2014)
2014
Cited alongside, same era.
Parikh, N., Boyd, S.P.: Proximal algorithms. Foundations and Trends in Optimization 1
2014
Cited alongside, same era.
Chen, Y., Pock, T.: Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence 39
2017
Later among the works it cites.
Gong, D., Yang, J., Liu, L., Zhang, Y., Reid, I.D., Shen, C., van den Hengel, A., Shi, Q.: From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 3806–3815 (2017)
2017
Later among the works it cites.
Kobler, E., Klatzer, T., Hammernik, K., Pock, T.: Variational networks: Connecting variational methods and deep learning. In: Proceedings of the German Conference on Pattern Recognition. pp. 281–293 (2017)
2017
Later among the works it cites.
Kruse, J., Rother, C., Schmidt, U.: Learning to push the limits of efficient FFT-based image deconvolution. In: Proceedings of the International Conference on Computer Vision. pp. 4596–4604 (2017)
2017
Later among the works it cites.
Meinhardt, T., Möller, M., Hazirbas, C., Cremers, D.: Learning proximal operators: Using denoising networks for regularizing inverse imaging problems. In: Proceedings of the International Conference on Computer Vision. pp. 1799–1808 (2017)
2017
Later among the works it cites.
Zhang, J., Pan, J., Lai, W., Lau, R.W.H., Yang, M.: Learning fully convolutional networks for iterative non-blind deconvolution. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 6969–6977 (2017)
2017
Later among the works it cites.
Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep CNN denoiser prior for image restoration. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 2808–2817 (2017)
2017
Later among the works it cites.
Pan, J., Sun, D., Pfister, H., Yang, M.: Deblurring images via dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence 40
2018
Later among the works it cites.
Aljadaany, R., Pal, D.K., Savvides, M.: Douglas-rachford networks: Learning both the image prior and data fidelity terms for blind image deconvolution. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 10235–10244 (2019)
2019
Later among the works it cites.
Brooks, T., Barron, J.T.: Learning to synthesize motion blur. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 6840–6848 (2019)
2019
Later among the works it cites.
Gong, D., Zhang, Z., Shi, Q., van den Hengel, A., Shen, C., Zhang, Y.: Learning deep gradient descent optimization for image deconvolution. IEEE Transactions on Neural Networks and Learning Systems pp. 1–15 (2020)
2020
Closest in time.