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Arbitrarily shaped Point Spread Function (PSF) estimation for single image blind deblurring

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Abstract

The research paper focuses on a challenging task faced in blind image deblurring (BID). It relates to the estimation of arbitrarily shaped (nonparametric or complex shaped) point spread functions (PSFs) of motion blur caused by camera handshake. These PSFs exhibit much more complex shapes than their parametric counterparts and deblurring, in this case, requires intricate ways to estimate the blur and effectively remove it. This research work introduces a novel blind deblurring scheme visualized for deblurring images corrupted by arbitrarily shaped PSFs. It is based on genetic algorithm and utilizes the Blind/Reference-less Image Spatial QUality Evaluator (BRISQUE) measure as the fitness function for arbitrarily shaped PSF estimation. The proposed BID scheme has been compared with other state-of-the-art single image motion deblurring schemes as benchmarks. Validation has been carried out on the standard real-life blurred images. Results of both benchmark and real images are presented. For real-life blurred images, the proposed BID scheme using BRISQUE converges in close vicinity of the original blurring functions. However, the benchmark schemes fail to effectively restore the real blurred images. The proposed scheme surpasses on average of seven percent higher image quality as compared to the benchmark schemes.

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References

  1. Biemond, J., Lagendijk, R.L., Mersereau, R.M.: Iterative methods for image deblurring. Proc. IEEE 78(5), 856–883 (1990)

    Article  Google Scholar 

  2. Almeida, M.S.C., Almeida, L.B.: Blind and semi-blind deblurring of natural images. IEEE Trans. Image Process. 19(1), 36–52 (2010)

    Article  MathSciNet  Google Scholar 

  3. Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Trans. Gr. 25(3), 787–794 (2006)

    Article  Google Scholar 

  4. Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Gr. 27(3), 10 (2008)

    Article  Google Scholar 

  5. Whyte, O., Sivic, J., Zisserman, A., Ponce, J.: Non-uniform deblurring for shaken images. Int. J. Comput. Vis. 98(2), 168–186 (2012)

    Article  MathSciNet  Google Scholar 

  6. Whyte, O., Sivic, J., Zisserman, A.: Deblurring shaken and partially saturated images. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, pp. 745–752 (2011). https://doi.org/10.1109/ICCVW.2011.6130327

  7. Hirsch, M., Schuler, C. J., Harmeling, S., Schölkopf, B.: Fast removal of non-uniform camera shake. In: 2011 International Conference on Computer Vision, Barcelona, pp. 463–470 (2011). https://doi.org/10.1109/ICCV.2011.6126276

  8. Gupta, A., Joshi, N., Lawrence Zitnick, C., Cohen, M., Curless, B.: Single image deblurring using motion density functions. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol. 6311. Springer, Berlin, Heidelberg (2010)

  9. Fortunato, H.E., Oliveira, M.M.: Fast high-quality non-blind deconvolution using sparse adaptive priors. Vis. Comput. 30(6–8), 661–671 (2014)

    Article  Google Scholar 

  10. Bini, A., Bhat, M.: A nonlinear level set model for image deblurring and denoising. Vis. Comput. 30(3), 311–325 (2014)

    Article  Google Scholar 

  11. Feng, Q., Fei, H., Wencheng, W.: Blind image deblurring with reinforced use of edges. Vis. Comput. 35(6–8), 1081–1090 (2019)

    Article  Google Scholar 

  12. Zhang, X., Sun, F., Liu, G., Ma, Y.: Non-blind deblurring of structured images with geometric deformation. Vis. Comput. 31(2), 131–140 (2015)

    Article  Google Scholar 

  13. Tubbs, R.: Lucky Exposures: Diffraction Limited Astronomical Imaging through the Atmosphere. Ph.D dissertation (2003)

  14. Khan, A., Yin, H.: Efficient blind image deconvolution using spectral non-gaussianity. Integr. Computer-Aided Eng. 19(4), 331–340 (2012)

    Article  Google Scholar 

  15. Hussain, I.: Non-Gaussianity Based Image Deblurring and Denoising. Thesis (2008)

  16. Hyvarinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626–634 (1999)

    Article  Google Scholar 

  17. Hyvarinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Netw. 13(4–5), 411–430 (2000)

    Article  Google Scholar 

  18. Khan, A., Yin, H.: Spectral non-gaussianity for blind image deblurring. Yin, H., Wang, W., Rayward-Smith, V. (eds.) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol. 6936. Springer, Berlin, Heidelberg (2011)

  19. Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)

    Article  MathSciNet  Google Scholar 

  20. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013)

    Article  Google Scholar 

  21. Yin, H.J., Hussain, I.: Independent component analysis and non-gaussianity for blind image deconvolution and deblurring. Integr. Computer-Aided Eng. 15(3), 219–228 (2008)

    Article  Google Scholar 

  22. Khan, A., Yin, H.: Quality measures for blind image deblurring. In: 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings, Manchester, pp. 456–459 (2012). https://doi.org/10.1109/IST.2012.6295559

  23. Mittal, A., Moorthy, A.K., Bovik, A.C.: No-Reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012). https://doi.org/10.1109/TIP.2012.2214050

    Article  MathSciNet  MATH  Google Scholar 

  24. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013). https://doi.org/10.1109/LSP.2012.2227726

    Article  Google Scholar 

  25. Registrar Amazon, “Desktop Nexus Wallpapers”. www.desktopnexus.com. Accessed 21 Apr 2020

  26. MATLAB. version 7.10.0 (R2010a). The MathWorks Inc., Natick, Massachusetts (2010)

  27. Lai, W., Huang, J., Hu, Z., Ahuja, N., Yang, M.: A comparative study for single image blind deblurring. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1701–1709, (2016)

  28. Brunet, D., Vrscay, E.R., Wang, Z.: On the mathematical properties of the structural similarity index. IEEE Trans. Image Process. 21(4), 1488–1499 (2012)

    Article  MathSciNet  Google Scholar 

  29. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  30. Wang, Z., Li, Q.: Information content weighting for perceptual image quality assessment. IEEE Trans. Image Process. 20(5), 1185–1198 (2011)

    Article  MathSciNet  Google Scholar 

  31. Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)

    Article  Google Scholar 

  32. Khan, A.: Efficient Methodologies for Single-Image Blind Deconvolution and Deblurring. Ph. D dissertation (2014)

  33. Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Trans. Graph. 25(3), 787–794. https://doi.org/10.1145/1141911.1141956

  34. Bovik, A.C.: Laboratory for Image & Video Engineering, The University of Texas at Austn, USA. http://live.ece.utexas.edu/research/Quality

  35. Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., Matas, J.: Deblurgan: Blind motion deblurring using conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8183–8192 (2018)

  36. Tao, X., Gao, H., Shen, X., Wang, J., Jia, J.: Scale-recurrent network for deep image deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8174–8182 (2018)

  37. Nah, S., Hyun Kim, T., Mu Lee, K.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3883–3891 (2017)

  38. Mai, L., Liu, F.: Kernel fusion for better image deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 371–380 (2015)

  39. Kupyn, O., Martyniuk, T., Wu, J., Wang, Z.: Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 8878–8887 (2019)

  40. Ramakrishnan, S., Pachori, S., Gangopadhyay, A., Raman, S.: Deep generative filter for motion deblurring. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 2993–3000 (2017)

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Authors and Affiliations

  1. Department of Computer Systems Engineering, University of Engineering and Technology (UET), Peshawar, 25120, Pakistan

    Aftab Khan

  2. School of Electrical and Electronic Engineering, The University of Manchester, Manchester, M13 9PL, UK

    Hujun Yin

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  1. Aftab Khan
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  2. Hujun Yin
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Correspondence to Aftab Khan.

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Khan, A., Yin, H. Arbitrarily shaped Point Spread Function (PSF) estimation for single image blind deblurring. Vis Comput 37, 1661–1671 (2021). https://doi.org/10.1007/s00371-020-01930-5

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