Abstract
We present a novel progressive framework for blind image restoration. Common blind restoration schemes first estimate the blur kernel, then employ non-blind deblurring. However, despite recent progress, the accuracy of PSF estimation is limited. Furthermore, the outcome of non-blind deblurring is highly sensitive to errors in the assumed PSF. Therefore, high quality blind deblurring has remained a major challenge. In this work, we combine state of the art regularizers for the image and the PSF, namely the Mumford & Shah piecewise-smooth image model and the sparse PSF prior. Previous works that used Mumford & Shah image regularization were either limited to non-blind deblurring or semi-blind deblurring assuming a parametric kernel known up to an unknown parameter. We suggest an iterative progressive restoration scheme, in which the imperfectly deblurred output of the current iteration is fed back as input to the next iteration. The kernel representing the residual blur is then estimated, and used to drive the non-blind restoration component, leading to finer deblurring. Experimental results demonstrate rapid convergence, and excellent performance on a wide variety of blurred images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kotera, J., Šroubek, F., Milanfar, P.: Blind deconvolution using alternating maximum a posteriori estimation with heavy-tailed priors. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013, Part II. LNCS, vol. 8048, pp. 59–66. Springer, Heidelberg (2013)
Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. SIGGRAPH 25, 787–794 (2006)
Ambrosio, L., Tortorelli, V.M.: Approximation of functionals depending on jumps by elliptic functionals via \(\Gamma \)-convergence. Communications on Pure and Applied Mathematics 43, 999–1036 (1990)
Bar, L., Sochen, N.A., Kiryati, N.: Variational pairing of image segmentation and blind restoration. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3022, pp. 166–177. Springer, Heidelberg (2004)
Zheng, H., Hellwich, O.: Extended mumford-shah regularization in bayesian estimation for blind image deconvolution and segmentation. In: Reulke, R., Eckardt, U., Flach, B., Knauer, U., Polthier, K. (eds.) IWCIA 2006. LNCS, vol. 4040, pp. 144–158. Springer, Heidelberg (2006)
Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. In: SIGGRAPH (2008)
Bar, L., Sochen, N., Kiryati, N.: Semi-blind image restoration via Mumford-Shah regularization. IEEE Transactions on Image Processing 15, 483–493 (2006)
Zhu, X., Milanfar, P.: Stabilizing and deblurring atmospheric turbulence. In: ICCP (2011)
Gal, R., Kiryati, N., Sochen, N.: Progress in the restoration of image sequences degraded by atmospheric turbulence. Pattern Recognition Letters 48, 8–14 (2014)
Vogel, C.R., Oman, M.E.: Fast, robust total variation-based reconstruction of noisy, blurred images. IEEE Trans. Image Process 7, 813–824 (1998)
Zhou, Y., Komodakis, N.: A MAP-estimation framework for blind deblurring using high-level edge priors. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part II. LNCS, vol. 8690, pp. 142–157. Springer, Heidelberg (2014)
Mahpod, S., Yitzhaky, Y.: Compression of turbulence-affected video signals SPIE, 7444 (2009)
Oreifej, O., Li, X., Shah, M.: Simultaneous video stabilization and moving object detection in turbulence. IEEE Trans. PAMI. 35, 450–462 (2013)
Mumford, D., Shah, S.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure and Appl. Math. 42, 577–682 (1989)
Wiener, N.: Extrapolation, interpolation, and smoothing of stationary time series. MIT Press (1964)
Michaeli, T., Irani, M.: Blind deblurring using internal patch recurrence. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part III. LNCS, vol. 8691, pp. 783–798. Springer, Heidelberg (2014)
Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithms. In: CVPR (2009)
Gildenblat, J.: Fast GPU implementation of Mumford-Shah regularization Semi-blind image restoration. Unpublished M.Sc. project report, School of Electrical Engineering. Tel Aviv University (2012)
You, Y., Kaveh, M.: A Regularization Approach to Joint Blur Identification and Image Restoration. IEEE Trans. Image Processing 5, 416–428 (1996)
Chan, T., Wong, C.: Total Variation Blind Deconvolution. IEEE Trans. Image Processing 7, 370–375 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hanocka, R., Kiryati, N. (2015). Progressive Blind Deconvolution. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9257. Springer, Cham. https://doi.org/10.1007/978-3-319-23117-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-23117-4_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23116-7
Online ISBN: 978-3-319-23117-4
eBook Packages: Computer ScienceComputer Science (R0)