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Structured, Heteroscedastic, and Multinomial Data

Consistent Blind Image Deblurring Using Jump-Preserving Extrapolation

Pages 372-382 | Received 15 Jun 2018, Accepted 01 Sep 2019, Published online: 16 Oct 2019

References

  • Beck, A., and Teboulle, M. (2009), “A Fast Iterative Shrinkage-Thresholding Algorithm With Application to Wavelet-Based Image Deblurring,” in IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. ICASSP 2009, IEEE, pp. 693–696.
  • Carasso, A. S. (2001), “Direct Blind Deconvolution,” SIAM Journal on Applied Mathematics, 61, 1980–2007. DOI: 10.1137/S0036139999362592.
  • Chan, T. F., and Wong, C.-K. (1998), “Total Variation Blind Deconvolution,” IEEE Transactions on Image Processing, 7, 370–375.
  • Cho, H., Wang, J., and Lee, S. (2012), “Text Image Deblurring Using Text-Specific Properties,” in European Conference on Computer Vision, Springer, pp. 524–537.
  • Fan, J., and Gijbels, I. (1996), Local Polynomial Modelling and Its Applications (Vol. 66), London: Chapman & Hall.
  • Faulkner, K., Kotre, C., and Louka, M. (1989), “Veiling Glare Deconvolution of Images Produced by X-Ray Image Intensifiers,” in Third International Conference on Image Processing and Its Applications, 1989, IET, pp. 669–673.
  • Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., and Freeman, W. T. (2006), “Removing Camera Shake From a Single Photograph,” in ACM Transactions on Graphics (TOG) (Vol. 25), ACM, pp. 787–794. DOI: 10.1145/1141911.1141956.
  • Hall, P., and Qiu, P. (2007), “Blind Deconvolution and Deblurring in Image Analysis,” Statistica Sinica, 17, 1483–1509. DOI: 10.5705/ss.2014.054.
  • Joshi, M. V., and Chaudhuri, S. (2005), “Joint Blind Restoration and Surface Recovery in Photometric Stereo,” JOSA A, 22, 1066–1076. DOI: 10.1364/JOSAA.22.001066.
  • Kang, Y. (2018), “DRIP: Discontinuous Regression and Image Processing,” R Package Version 1.4.
  • Kang, Y., Mukherjee, P. S., and Qiu, P. (2018), “Efficient Blind Image Deblurring Using Nonparametric Regression and Local Pixel Clustering,” Technometrics, 6, 522–531. DOI: 10.1080/00401706.2017.1415975.
  • Krist, J. (1995), “Simulation of HST PSFs using Tiny Tim,” in Astronomical Data Analysis Software and Systems IV (Vol. 77), p. 349.
  • Levin, A., Weiss, Y., Durand, F., and Freeman, W. T. (2011), “Understanding Blind Deconvolution Algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 2354–2367. DOI: 10.1109/TPAMI.2011.148.
  • Li, T.-H., and Lii, K.-S. (2002), “A Joint Estimation Approach for Two-Tone Image Deblurring by Blind Deconvolution,” IEEE Transactions on Image Processing, 11, 847–858.
  • Nishiyama, M., Hadid, A., Takeshima, H., Shotton, J., Kozakaya, T., and Yamaguchi, O. (2011), “Facial Deblur Inference Using Subspace Analysis for Recognition of Blurred Faces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 838–845. DOI: 10.1109/TPAMI.2010.203.
  • Pan, J., Hu, Z., Su, Z., and Yang, M.-H. (2014a), “Deblurring Face Images With Exemplars,” in European Conference on Computer Vision, Springer, pp. 47–62.
  • Pan, J., Hu, Z., Su, Z., and Yang, M.-H. (2014b), “Deblurring Text Images via L0-Regularized Intensity and Gradient Prior,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 2901–2908.
  • Perrone, D., and Favaro, P. (2016), “A Clearer Picture of Total Variation Blind Deconvolution,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 1041–1055. DOI: 10.1109/TPAMI.2015.2477819.
  • Qiu, P. (2005), Image Processing and Jump Regression Analysis (Vol. 599), New York: Wiley.
  • Qiu, P. (2009), “Jump-Preserving Surface Reconstruction From Noisy Data,” Annals of the Institute of Statistical Mathematics, 61, 715–751. DOI: 10.1007/s10463-007-0166-9.
  • Qiu, P., and Kang, Y. (2015), “Blind Image Deblurring Using Jump Regression Analysis,” Statistica Sinica, 25, 879–899. DOI: 10.5705/ss.2014.054.
  • R Core Team (2019), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing.
  • Schulz, T. J. (1993), “Multiframe Blind Deconvolution of Astronomical Images,” JOSA A, 10, 1064–1073. DOI: 10.1364/JOSAA.10.001064.
  • You, Y.-L., and Kaveh, M. (1996), “Anisotropic Blind Image Restoration,” in International Conference on Image Processing, 1996. Proceedings (Vol. 2), IEEE, pp. 461–464.

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