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PSF Estimation of Simple Lens Based on Circular Partition Strategy

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Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12890))

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Abstract

Recently, single-lens computational imaging has gradually become a new research direction of computational photography, which combines the front-end simple optical imaging equipment with the late image restoration algorithm to obtain high-quality images. Single lens computational imaging is essentially a problem of image restoration. The estimation accuracy of point spread function (PSF) will directly affect the quality of image restoration. Existing spatially variant PSF estimation methods usually divide images into rectangular blocks. Considering the imaging characteristics of single lens, a PSF estimation method based on a circular partition strategy is proposed in this paper. Experimental results show that this segmented method can achieve better PSF estimation accuracy and improve image restoration quality.

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References

  1. Harris, J.R., Battiste, D.R., Bertus, B.J.: Cracking catalysts comprising pillared clays (1988)

    Google Scholar 

  2. Heide, F., Rouf, M., Hullin, M.B., Labitzke, B., Kolb, A.: High-quality computational imaging through simple lenses. ACM Trans. Graph. 32(5), 149 (2013)

    Article  Google Scholar 

  3. Cui, J., Huang, W.: Optical aberration correction for simple lenses via sparse representation. Optics Commun. 401, 201–213 (2018). (A Journal Devoted to the Rapid Publication of Short Contributions in the Field of Optics & Interaction of Light with Matter )

    Google Scholar 

  4. Kim, T.H., Ahn, B., Lee, K.M.: Dynamic scene deblurring. In: IEEE International Conference on Computer Vision (2013)

    Google Scholar 

  5. Kim, T.H., Lee, K.M.: Segmentation-free dynamic scene deblurring. In: Computer Vision & Pattern Recognition (2014)

    Google Scholar 

  6. Lee, S., Cho, S.: Recent advances in image deblurring. In: Siggraph Asia, pp. 1–108 (2013)

    Google Scholar 

  7. Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithms. IEEE (2009)

    Google Scholar 

  8. Li, X., Zheng, S., Jia, J.: Unnatural l0 sparse representation for natural image deblurring. In: IEEE Conference on Computer Vision & Pattern Recognition (2013)

    Google Scholar 

  9. Lu, Y., Jian, S., Long, Q., Shum, H.Y.: Image deblurring with blurred/noisy image pairs. ACM Trans. Graph. 26(3), 1 (2007)

    Article  Google Scholar 

  10. Lucy, L.B.: An iterative technique for the rectification of observed distributions. Astron. J. 79(6), 745 (1974)

    Article  Google Scholar 

  11. Mahajan, V.N.: Aberration theory made simple. Books in Canada (2011)

    Google Scholar 

  12. Moorthy, A.K., Bovik, A.: A two-step framework for constructing blind image quality indices. IEEE Sig. Process. Lett. 17, 513–516 (2010)

    Article  Google Scholar 

  13. Peng, Y., Sun, Q., Xiong, D., Wetzstein, G., Heide, F.: Learned large field-of-view imaging with thin-plate optics. ACM Trans. Graph. 38(6), 1–14 (2019)

    Google Scholar 

  14. Richardson, W.H.: Bayesian-based iterative method of image restoration. J. Opt. Soc. Am. 62, 55–59 (1972)

    Article  Google Scholar 

  15. Schuler, C.J., Hirsch, M., Harmeling, S., Schlkopf, B.: Non-stationary correction of optical aberrations. In: IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, 6–13 November, 2011 (2011)

    Google Scholar 

  16. Smith, B., Lysenko, M.: Image deblurring (2009)

    Google Scholar 

  17. Stockham, T.G., Cannon, T.M., Ingebretsen, R.B.: Blind deconvolution through digital signal processing. Proc. IEEE 63(4), 678–692 (1975). https://doi.org/10.1109/PROC.1975.9800

    Article  Google Scholar 

  18. Wiener, N.: The extrapolation, interpolation and smoothing of stationary time series, with engineering applications. J. R. Statist. Soc. A (General) 113(3), 413 (1950)

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Yue, T., Suo, J., Xiao, Y., Zhang, L., Dai, Q.: Image quality enhancement using original lens via optical computing. Opt. Express 22(24), 29515–30 (2014)

    Article  Google Scholar 

  21. Zhan, D., Li, W., Yin, X., Niu, C., Liu, J.: PSF estimation method of simple-lens camera using normal sinh-arcsinh model based on noise image pairs. IEEE Access 9, 49338–49353 (2021)

    Google Scholar 

Download references

Acknowledge

This research was partially supported by National Basic Enhancement Research Program of China under key basic research project, National Natural Science Foundation (NSFC) of China under project No. 61906206, 62071478.

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Authors and Affiliations

  1. College of Systems Engineering, National University of Defense Technology, Changsha, China

    Hanxiao Cai, Weili Li, Maojun Zhang, Zheng Zhang & Wei Xu

Authors
  1. Hanxiao Cai
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  2. Weili Li
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  3. Maojun Zhang
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  4. Zheng Zhang
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  5. Wei Xu
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Corresponding author

Correspondence to Wei Xu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

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Cai, H., Li, W., Zhang, M., Zhang, Z., Xu, W. (2021). PSF Estimation of Simple Lens Based on Circular Partition Strategy. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12890. Springer, Cham. https://doi.org/10.1007/978-3-030-87361-5_42

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  • DOI: https://doi.org/10.1007/978-3-030-87361-5_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87360-8

  • Online ISBN: 978-3-030-87361-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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