“Progressive inter-scale and intra-scale non-blind image deconvolution” by Yuan, Sun, Quan and Shum

  • ©Lu Yuan, Jian Sun, Long Quan, and Heung-Yeung Shum




    Progressive inter-scale and intra-scale non-blind image deconvolution



    Ringing is the most disturbing artifact in the image deconvolution. In this paper, we present a progressive inter-scale and intra-scale non-blind image deconvolution approach that significantly reduces ringing. Our approach is built on a novel edge-preserving deconvolution algorithm called bilateral Richardson-Lucy (BRL) which uses a large spatial support to handle large blur. We progressively recover the image from a coarse scale to a fine scale (inter-scale), and progressively restore image details within every scale (intra-scale). To perform the inter-scale deconvolution, we propose a joint bilateral Richardson-Lucy (JBRL) algorithm so that the recovered image in one scale can guide the deconvolution in the next scale. In each scale, we propose an iterative residual deconvolution to progressively recover image details. The experimental results show that our progressive deconvolution can produce images with very little ringing for large blur kernels.


    1. Agrawal, A., and Raskar, R. 2007. Resolving objects at higher resolution from a single motion-blurred image. In Proceedings of CVPR, 1–8.Google Scholar
    2. Banham, M. R., and Katsaggelos, A. K. 1997. Digital image restoration. IEEE Signal Processing Magazine 42, 2, 24–41.Google ScholarCross Ref
    3. Bar, L., Sochen, N., and Kiryati, N. 2006. Semi-blind image restoration via mumford-shah regularization. IEEE Trans. on Image Processing. 15, 2, 483–493. Google ScholarDigital Library
    4. Bardsley, J., Jefferies, S., Nagy, J., and Plemmons, R. 2006. Blind iterative restoration of images with spatially-varying blur. In Optics Express, 1767–1782.Google Scholar
    5. Ben-Ezra, M., and Nayar, S. K. 2003. Motion deblurring using hybrid imaging. In Proceedings of CVPR, vol. I, 657–664. Google ScholarDigital Library
    6. Black, M. J., Sapiro, G., Marimont, D. H., and Heeger, D. 1998. Robust anisotropic diffusion. IEEE Trans. on Image Processing 7, 3, 421–432. Google ScholarDigital Library
    7. Caron, J. N., M., N. N., and J., R. C. 2002. Noniterative blind data restoration by use of an extracted filter function. Applied optics (Appl. opt.) 41, 32, 68–84.Google Scholar
    8. Dey, N., Blanc-Fraud, L., Zimmer, C., Kam, Z., Roux, P., Olivo-Marin, J., and Zerubia., J. 2006. Richardson-lucy algorithm with total variation regularization for 3d confocal microscope deconvolution. Microscopy Research Technique 26, 69, 260–266.Google ScholarCross Ref
    9. Dowski, E. R., and Johnson, G. E. 1999. Wavefront coding: A modern method of achieving high performance and/or low cost imaging systems. In SPIE, vol. 29, 137–145.Google Scholar
    10. Durand, F., and Dorsey, J. 2002. Fast bilateral filtering for the display of high-dynamic-range images. In Proceedings of SIGGRAPH, 257–266. Google ScholarDigital Library
    11. Eisemann, E., and Durand, F. 2004. Flash photography enhancement via intrinsic relighting. ACM Trans. on Graph. (SIGGRAPH) 23, 3, 673–678. Google ScholarDigital Library
    12. Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., and Freeman, W. T. 2006. Removing camera shake from a single photograph. ACM Trans. on Graph. (SIGGRAPH) 25, 3, 787–794. Google ScholarDigital Library
    13. Figueiredo, M., Bioucas-Dias, J., and Nowak, R. 2007. Majorization-minimization algorithms for wavelet-based image restoration. IEEE Trans. on Image Processing 16, 12, 2980–2991. Google ScholarDigital Library
    14. Geman, D., and Reynolds, G. 1992. Constrained restoration and the recovery of discontinuities. IEEE Trans. on PAMI. 14, 3, 367–383. Google ScholarDigital Library
    15. Geman, D., and Yang, C. 1995. Nonlinear image recovery with half-quadratic regularization. IEEE Trans. on Image Processing 4, 7, 932–946. Google ScholarDigital Library
    16. Green, P., Sun, W., Matusik, W., and Durand, F. 2007. Multi-aperture photography. ACM Trans. on Graph. (SIGGRAPH) 26, 6, 68–75. Google ScholarDigital Library
    17. Invensense.com. http://www.invensense.com/.Google Scholar
    18. Jia, J. 2007. Single image motion deblurring using transparency. In Proceedings of CVPR, 1141–1151.Google ScholarCross Ref
    19. Kopf, J., Cohen, M., Lischinski, D., and Uyttendaele, M. 2007. Joint bilateral upsampling. ACM Trans. on Graph. (SIGGRAPH) 26, 3, 96–99. Google ScholarDigital Library
    20. Kundur, D., and Hatzinakos, D. 1996. Blind image deconvolution. IEEE Signal Processing Magazine. 13, 3, 43–64.Google ScholarCross Ref
    21. Levin, A., Fergus, R., Durand, F., and Freeman, W. T. 2007. Image and depth from a conventional camera with a coded aperture. ACM Trans. on Graph. (SIGGRAPH) 26, 6, 70–77. Google ScholarDigital Library
    22. Levin, A. 2006. Blind motion deblurring using image statistics. In Advances in Neural Information Processing Systems 19, 841–848.Google Scholar
    23. Levoy, M., Ng, R., Adams, A., Footer, M., and Horowitz, M. 2006. Light field microscopy. ACM Trans. on Graph. (SIGGRAPH) 25, 3, 68. Google ScholarDigital Library
    24. Lucy, L. 1974. An iterative technique for the rectification of observed distributions. Astronomical Journal 79, 745.Google ScholarCross Ref
    25. Mignotte, M. 2006. A segmentation-based regularization term for image deconvolution. IEEE Trans on Image Processing 15, 7, 1973–1984.Google ScholarDigital Library
    26. Murtagh, F., Starck, J. L., and Bijaoui., A. 1995. Image restoration with noise suppression using a multiresolution support. Astronomy and Astrophysics, 112, 179–189.Google Scholar
    27. Neelamani, R., Choi, H., and Baraniuk, R. 2004. ForWaRd: Fourier-wavelet regularized deconvolution for ill-conditioned systems. IEEE Trans. on Signal Processing 52, 2, 418–433. Google ScholarDigital Library
    28. Petschnigg, G., Agrawala, M., Hoppe, H., Szeliski, R., Cohen, M., and Toyama., K. 2004. Digital photography with flash and no-flash image pairs. ACM Trans. on Graph. (SIGGRAPH) 23, 3, 664–672. Google ScholarDigital Library
    29. Raskar, R., Agrawal, A., and Tumblin, J. 2006. Coded exposure photography: motion deblurring using fluttered shutter. ACM Trans. on Graph. (SIGGRAPH) 25, 3, 795–804. Google ScholarDigital Library
    30. Reeves, S. J., and Mersereau, R. M. 1992. Blur identification by the method of generalized cross-validation. IEEE Trans. on Image Processing. 1, 3, 301–311.Google ScholarDigital Library
    31. Rudin, L., Osher, S., and Fatemi, E. 1992. Nonlinear total variation based noise removal algorithms. Physica D 60. Google ScholarDigital Library
    32. Terzopoulos, D. 1986. Regularization of inverse visual problems involving discontinuities. IEEE Trans. on PAMI 8, 4, 413–242. Google ScholarDigital Library
    33. Tikhonov, A. 1943. On the stability of inverse problems. Dokl. Akad. Nauk SSSR 39, 5, 195–198.Google Scholar
    34. Tomasi, C., and Manduchi, R. 1998. Bilateral filtering for gray and color images. In Proceedings of ICCV, 839–847. Google ScholarDigital Library
    35. Veeraraghavan, A., Raskar, R., Agrawal, A., Mohan, A., and Tumblin, J. 2007. Dappled photography: mask enhanced cameras for heterodyned light fields and coded aperture refocusing. ACM Trans. on Graph. (SIGGRAPH) 26, 6, 69–76. Google ScholarDigital Library
    36. Yuan, L., Sun, J., Quan, L., and Shum, H.-Y. 2007. Image deblurring with blurred/noisy image pairs. ACM Trans. on Graph. (SIGGRAPH) 26, 3, 1–10. Google ScholarDigital Library
    37. Zand, J. 1996. Coded aperture imaging in high energy astronomy. NASA Laboratory for High Energy Astrophysics (LHEA) at NASA’s GSFC.Google Scholar

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