Abstract
In this paper a parametric point spread function (PSF) estimation method is presented. This method can be employed for estimating the parameters of linear motion blur vector. The proposed method works on single image and estimates angle and length of motion blur vector to generate required PSF for deblurring. This method is based on step-by-step estimation of motion blur vector. In the first approximation the blind method considers short length vectors in all directions and deblurs image with PSF of these candidate vectors. The quality of deblurred image is assessed by a no-reference quality measurement metric which is proposed in this paper. The proposed no-reference image quality metric evaluates degradation in sharpness of edges and the amount of resulted artifact caused by saturated pixels, in the deblurred image. Those motion vectors that caused unacceptable deblurring results are omitted in the next iteration. The approximation is improved by increasing the length of the remaining vectors and the same process continues iteratively. The process goes on until only one vector remains as the estimation for motion blur vector. Experimental results show that in estimation of motion blur vectors, the length estimation error is less than one pixel in 85% of the cases and angle estimation error in 95% of the cases is less than one degree. Comparing with a conventional method indicates that the proposed method shows more than four times improvement in length estimation and 10% improvement in angle estimation. Moreover, the proposed method has comparatively lower computational load than other conventional deblurring methods.
Similar content being viewed by others
References
Adam T, Paramesran R (2020) Hybrid non-convex second-order total variation with applications to non-blind image deblurring. SIViP 14(1):115–123. https://doi.org/10.1007/s11760-019-01531-3
Babacan SD, Molina R, Do MN, Katsaggelos AK (2012). Bayesian blind deconvolution with general sparse image priors. In: European Conference on Computer Vision. Springer, pp 341–355. doi:https://doi.org/10.1007/978-3-642-33783-3_25
Bai Y, Cheung G, Liu X, Gao W (2019) Graph-based blind image Deblurring from a single photograph. IEEE Trans Image Process 28:1404–1418. https://doi.org/10.1109/TIP.2018.2874290
Brusius F, Schwanecke U, Barth P (2011). Blind image deconvolution of linear motion blur. In: International Conference on Computer Vision, Imaging and Computer Graphics. Springer, pp 105–119. doi:https://doi.org/10.1007/978-3-642-32350-8_7
Dobeš M, Machala L, Fürst T (2010) Blurred image restoration: a fast method of finding the motion length and angle. Digital Signal Processing 20(6):1677–1686. https://doi.org/10.1016/j.dsp.2010.03.012
Elmi Y, Zargari F, Rahmani AM (2018) Blind image Deblurring based on multi-resolution ringing removal. Signal Process 154:250–259. https://doi.org/10.1016/j.sigpro.2018.09.015
Gonzalez RC, Woods RE (2017). Digital image processing. Pearson,
Gregson J, Heide F, Hullin MB, Rouf M, Heidrich W (2013) Stochastic deconvolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 1043–1050. doi: https://doi.org/10.1109/CVPR.2013.139
Javaran TA, Hassanpour H, Abolghasemi V (2019) Blind motion image deblurring using an effective blur kernel prior. Multimed Tools Appl 78(16):22555–22574. https://doi.org/10.1007/s11042-019-7402-1
Khan M, Nizami IF, Majid M (2019) No-reference image quality assessment using gradient magnitude and wiener filtered wavelet features. Multimed Tools Appl 78(11):14485–14509. https://doi.org/10.1007/s11042-018-6797-4
Krishnan D, Tay T, Fergus R (2011). Blind deconvolution using a normalized sparsity measure. In: IEEE Conference on Computer Vision and Pattern Recognition . IEEE, pp 233–240. doi:https://doi.org/10.1109/CVPR.2011.5995521
Li Y, Tofighi M, Geng J, Monga V, Eldar YC (2020) Efficient and interpretable deep blind image Deblurring via algorithm unrolling. IEEE Transactions on Computational Imaging 6:666–681. https://doi.org/10.1109/TCI.2020.2964202
Liu Y, Wang J, Cho S, Finkelstein A, Rusinkiewicz S (2013) A no-reference metric for evaluating the quality of motion deblurring. ACM Trans Graph 32(6):1–12. https://doi.org/10.1145/2508363.2508391
Ma C, Zhang J, Xu S, Meng W, Xi R, Kumar GH, Zhang X (2018) Accurate blind deblurring using salientpatch-based prior for large-size images. Multimed Tools Appl 77(21):28077–28100. https://doi.org/10.1007/s11042-018-6009-2
Marziliano P, Dufaux F, Winkler S, Ebrahimi T A (2002) No-reference perceptual blur metric. In: Proceedings of International Conference on Image Processing. IEEE, pp 57–60 vol. 3. doi:https://doi.org/10.1109/ICIP.2002.1038902
Mesarovic VZ, Galatsanos NP, Katsaggelos AK (1995) Regularized constrained total least squares image restoration. IEEE Trans Image Process 4(8):1096–1108. https://doi.org/10.1109/83.403444
Mosleh A, Elmi Y, Zargari F, Onzon E, Langlois JP (2018) Explicit ringing removal in image Deblurring. IEEE Trans Image Process 27(2):580–593. https://doi.org/10.1109/TIP.2017.2764625
Mosleh A, Green P, Onzon E, Begin I, Pierre Langlois J (2015). Camera intrinsic blur kernel estimation: A reliable framework. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 4961–4968. doi: https://doi.org/10.1109/CVPR.2015.7299130
Mosleh A, Langlois JP, Green P (2014). Image deconvolution ringing artifact detection and removal via PSF frequency analysis. In: European Conference on Computer Vision. Springer, pp 247–262. doi:https://doi.org/10.1007/978-3-319-10593-2_17
Nah S, Kim TH, Lee KM (2017). Deep multi-scale convolutional neural network for dynamic scene deblurring. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 3883–3891. doi: https://doi.org/10.1109/CVPR.2017.35
Nayar S, Ben-Ezra M (2004) Motion-based motion deblurring. IEEE Trans Pattern Anal Mach Intell 26(6):689–698. https://doi.org/10.1109/TPAMI.2004.1
Oliveira JP, Figueiredo MA, Bioucas-Dias JM (2014) Parametric blur estimation for blind restoration of natural images: linear motion and out-of-focus. IEEE Trans Image Process 23(1):466–477. https://doi.org/10.1109/TIP.2013.2286328
Pan J, Hu Z, Su Z, Yang M-H (2014). Deblurring text images via L0-regularized intensity and gradient prior. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 2901–2908. doi: https://doi.org/10.1109/CVPR.2014.371
Rezaie F, Helfroush MS, Danyali H (2018) No-reference image quality assessment using local binary pattern in the wavelet domain. Multimed Tools Appl 77(2):2529–2541. https://doi.org/10.1007/s11042-017-4432-4
Richardson WH (1972) Bayesian-based iterative method of image restoration. J Opt Soc Am 62(1):55–59. https://doi.org/10.1364/JOSA.62.000055
Sindelar O, Sroubek F, Milanfar P (2014). Space-variant image deblurring on smartphones using inertial sensors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. IEEE, pp 191–192. doi: https://doi.org/10.1109/CVPRW.2014.34
Soe AK, Zhang X A (2012). simple PSF parameters estimation method for the de-blurring of linear motion blurred images using wiener filter in OpenCV. In: International Conference on Systems and Informatics (ICSAI). IEEE, pp 1855–1860. doi:https://doi.org/10.1109/ICSAI.2012.6223408
Su S, Delbracio M, Wang J, Sapiro G, Heidrich W, Wang O (2017). Deep video deblurring for hand-held cameras. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 1279–1288 doi: https://doi.org/10.1109/CVPR.2017.33
Xu L, Jia J (2010). Two-phase kernel estimation for robust motion deblurring. In: European Conference on Computer Vision. Springer, pp 157–170. doi:https://doi.org/10.1007/978-3-642-15549-9_12
Xue F, Blu T (2015) A novel SURE-based criterion for parametric PSF estimation. IEEE Trans Image Process 24(2):595–607. https://doi.org/10.1109/TIP.2014.2380174
Yu J, Chang Z, Xiao C (2019) Blur kernel estimation using sparse representation and cross-scale self-similarity. Multimed Tools Appl 78(13):18549–18570. https://doi.org/10.1007/s11042-019-7237-9
Zhang F, Lu W, Liu H, Xue F (2018) Natural image deblurring based on l0-regularization and kernel shape optimization. Multimed Tools Appl 77(20):26239–26257. https://doi.org/10.1007/s11042-018-5847-2
Zhu X, Šroubek F, Milanfar P (2012). Deconvolving psfs for a better motion deblurring using multiple images. In: European Conference on Computer Vision. Springer, pp 636–647. doi:https://doi.org/10.1007/978-3-642-33715-4_46
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Elmi, Y., Zargari, F. & Rahmani, A.M. Iterative approach for parametric PSF estimation. Multimed Tools Appl 79, 29433–29450 (2020). https://doi.org/10.1007/s11042-020-09511-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09511-3