article

Fast high-quality non-blind deconvolution using sparse adaptive priors

Authors:

Horacio E. Fortunato,

Manuel M. OliveiraAuthors Info & Claims

The Visual Computer: International Journal of Computer Graphics, Volume 30, Issue 6-8

Pages 661 - 671

https://doi.org/10.1007/s00371-014-0966-x

Published: 01 June 2014 Publication History

Abstract

We present an efficient approach for high-quality non-blind deconvolution based on the use of sparse adaptive priors. Its regularization term enforces preservation of strong edges while removing noise. We model the image-prior deconvolution problem as a linear system, which is solved in the frequency domain. This clean formulation lends to a simple and efficient implementation. We demonstrate its effectiveness by performing an extensive comparison with existing non-blind deconvolution methods, and by using it to deblur photographs degraded by camera shake. Our experiments show that our solution is faster and its results tend to have higher peak signal-to-noise ratio than the state-of-the-art techniques. Thus, it provides an attractive alternative to perform high-quality non-blind deconvolution of large images, as well as to be used as the final step of blind-deconvolution algorithms.

References

[1]

Banham, M., Katsaggelos, A.: Digital image restoration. Signal Process. Mag. IEEE 14(2), 4---41 (1997)

[2]

Ben-Ezra, M., Nayar, S.: Motion-based Motion Deblurring. IEEE TPAMI 26(6), 689---698 (2004)

[3]

Bonesky, T.: Morozov's discrepancy principle and tikhonov-type functionals. Inverse Probl. 25(1), 015015 (2009)

[4]

Cai, J.-F., Ji, H., Liu, C., Shen, Z.: Blind motion deblurring from a single image using sparse approximation. In: CVPR 2009. pp 104---111 (2009)

[5]

Campisi, P., Egiazarian, K.: Blind Image Deconvolution: theory and Applications. CRC Press, Boca Raton (2007)

[6]

Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. 5(28), 145 (2009). (1---145:8)

[7]

Cho, S., Wang, J., Lee, S.: Handling outliers in non-blind image deconvolution. ICCV 2011. pp 1---8 (2011)

Digital Library

[8]

Cho, T.S., Zitnick, C.L., Joshi, N., Kang, S.B., Szeliski, R., Freeman, W.T.: Image restoration by matching gradient distributions. IEEE TPAMI 34(4), 683---694 (2012)

Digital Library

[9]

Engl, H., Hanke, M., Neubauer, A.: Regularization of Inverse Problems. Kluwer, Dordrecht (1996)

[10]

Gastal, E.S.L., Oliveira, M.M.: Domain transform for edge-aware image and video processing. ACM TOG 30(4), 69 (2011). (1---12, SIGGRAPH 2011)

Digital Library

[11]

Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM TOG 25, 787---794 (2006)

Digital Library

[12]

Fortunato, H.E., Oliveira, M.M.: Coding depth through mask structure. Computer graphics forum. In: Proceedings of Eurographics, vol. 31(2), pp. 459---468 (2012)

[13]

Geman, D., Yang, C.: Nonlinear image recovery with half-quadratic regularization. Image Process. IEEE Trans. 4(7), 932---946 (1995)

Digital Library

[14]

Joshi, N., Zitnick, C., Szeliski, R., Kriegman, D.: Image deblurring and denoising using color priors. In: CVPR 2009, pp. 1550---1557 (2009)

[15]

Kodak. Kodak Lossless True Color Image Suite. http://r0k.us/graphics/kodak/. Accessed Jan 2013

[16]

Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-laplacian priors. Adv. Neural Inf. Process. Syst. 22, 1033---1041 (2009)

[17]

Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. ACM TOG, vol. 26 (article 70) (2007)

Digital Library

[18]

Levin, A., Sand, P., Cho, T.S., Durand, F., Freeman, W.T.: Motion-invariant photography. ACM Trans. Graph. 27, 71 (2008). (1---71:9)

Digital Library

[19]

Levin, A., Weiss, Y., Durand, F., Freeman, W.: Efficient marginal likelihood optimization in blind deconvolution. In: CVPR 2011, pp. 2657---2664 (2011)

Digital Library

[20]

Liu, R., Jia, J.: Reducing boundary artifacts in image deconvolution. In: ICIP'08, pp. 505---508 (2008)

[21]

Lucy, L.B.: An iterative technique for the rectification of observed distributions. Astron. J. 79, 745 (1974)

[22]

Shan, Q., Xiong, W., Jia, J.: Rotational motion deblurring of a rigid object from a single image. In: ICCV 2007, pp. 1---8 (2007)

[23]

Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM TOG 27 (2008)

Digital Library

[24]

Sibarita, J.B.: Deconvolution microscopy. Adv. Biochem. Eng. Biotechnol. 95, 201---243 (2005)

[25]

Starck, J.L., Pantin, E., Murtagh, F.: Deconvolution in astronomy: a review. Publications of the Astronomical Society of the Pacific (October), pp. 1051---1069 (2002)

[26]

Tai, Y.-W., Chen, X., Kim, S., Kim, S.J., Li, F., Yang, J., Yu, J., Matsushita, Y., Brown, M.S.: Nonlinear camera response functions and image deblurring: theoretical analysis and practice. IEEE TPAMI 2013 35, 2498---2512 (2013)

[27]

Tikhonov, A.N., Arsenin, V.Y.: Solutions of Ill-Posed Problems. Wiley, New York (1977)

[28]

Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. ICCV '98, pp. 839---846 (1998)

[29]

Wang, Y., Yang, J., Yin, W., Zhang, Y.: A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Imaging Sci. 1(3), 248---272 (2008)

Digital Library

[30]

Yuan, L., Sun, J., Quan, L., Shum, H.-Y.: Progressive inter-scale and intra-scale non-blind image deconvolution. ACM Trans. Graph. 27(3), 74 (2008). (1---74:10)

Digital Library

[31]

Xu, L., Jia, J.: Two-phase Kernel Estimation for Robust Motion Deblurring ECCV'10, pp. 157---170 (2010)

[32]

Xu, L., Zheng, S., Jia, J.: Unnatural L0 Sparse Representation for Natural Image Deblurring CVPR, pp. 1107---1114 (2013)

[33]

Zhou, C., Nayar, S.K.: What are good apertures for defocus deblurring? In: ICCP, pp. 1---8 (2009)

Cited By

Fu BFu SWu YMao YRen YThanh D(2024)Deep non-blind deblurring network for saturated blurry imagesNeural Computing and Applications10.1007/s00521-024-09495-336:14(7829-7843)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s00521-024-09495-3
Wang HHu CQian WWang Q(2024)RT-Deblur: real-time image deblurring for object detectionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02991-y40:4(2873-2887)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s00371-023-02991-y
Karaali AHarte NJung C(2022)Deep Multi-Scale Feature Learning for Defocus Blur EstimationIEEE Transactions on Image Processing10.1109/TIP.2021.313924331(1097-1106)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TIP.2021.3139243
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Fast high-quality non-blind deconvolution using sparse adaptive priors
1. Computing methodologies
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cover image The Visual Computer: International Journal of Computer Graphics

The Visual Computer: International Journal of Computer Graphics Volume 30, Issue 6-8

June 2014

379 pages

ISSN:0178-2789

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Copyright © Copyright © 2014 Springer-Verlag Berlin Heidelberg.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 June 2014

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Cited By

Fu BFu SWu YMao YRen YThanh D(2024)Deep non-blind deblurring network for saturated blurry imagesNeural Computing and Applications10.1007/s00521-024-09495-336:14(7829-7843)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s00521-024-09495-3
Wang HHu CQian WWang Q(2024)RT-Deblur: real-time image deblurring for object detectionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02991-y40:4(2873-2887)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s00371-023-02991-y
Karaali AHarte NJung C(2022)Deep Multi-Scale Feature Learning for Defocus Blur EstimationIEEE Transactions on Image Processing10.1109/TIP.2021.313924331(1097-1106)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TIP.2021.3139243
Jiang ZZhang ZYu YLiu R(2022)Bilevel modeling investigated generative adversarial framework for image restorationThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-022-02681-139:11(5563-5575)Online publication date: 15-Oct-2022
https://dl.acm.org/doi/10.1007/s00371-022-02681-1
Khan AYin H(2021)Arbitrarily shaped Point Spread Function (PSF) estimation for single image blind deblurringThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-020-01930-537:7(1661-1671)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s00371-020-01930-5
Javaran THassanpour HAbolghasemi V(2019)Blind motion image deblurring using an effective blur kernel priorMultimedia Tools and Applications10.1007/s11042-019-7402-178:16(22555-22574)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s11042-019-7402-1
Li TGharbi MAdams ADurand FRagan-Kelley J(2018)Differentiable programming for image processing and deep learning in halideACM Transactions on Graphics10.1145/3197517.320138337:4(1-13)Online publication date: 30-Jul-2018
https://dl.acm.org/doi/10.1145/3197517.3201383
Cholewiak SLove GSrinivasan PNg RBanks M(2017)ChromablurACM Transactions on Graphics10.1145/3130800.313081536:6(1-12)Online publication date: 20-Nov-2017
https://dl.acm.org/doi/10.1145/3130800.3130815
Hach TSteurer JAmruth APappenheim ACollomosse J(2015)Cinematic Bokeh rendering for real scenesProceedings of the 12th European Conference on Visual Media Production10.1145/2824840.2824842(1-10)Online publication date: 24-Nov-2015
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Montalto CGarcia-Dorado IAliaga DOliveira MMeng F(2015)A Total Variation Approach for Customizing Imagery to Improve Visual AcuityACM Transactions on Graphics10.1145/271730734:3(1-16)Online publication date: 8-May-2015
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