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RDEPS: A Combined Reaction-Diffusion Equation and Photometric Similarity Filter for Optical Image Restoration

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Advances in Visual Computing (ISVC 2016)

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Abstract

Restoration of optical images degraded by atmospheric turbulence and various types of noise is still an open problem. In this paper, we propose an optical image restoration method based on a Reaction-Diffusion Equation and Photometric Similarity (RDEPS). We exploit photometric similarity and geometric closeness of the optical image by combining a photometric similarity function and a appropriately defined reaction-diffusion equation. Our resulting RDEPS filter is used to restore images degraded by atmospheric turbulence and noise, including Gaussian noise and impulse noise. Extensive experimental results show that our method outperforms other recently developed methods in terms of PSNR and SSIM. Moreover, the computational efficiency analysis shows that our RDEPS provides efficient restoration of optical images.

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References

  1. Anantrasirichai, N., Achim, A., Kingsbury, N.G., Bull, D.R.: Atmospheric turbulence mitigation using complex wavelet-based fusion. IEEE Trans. Image Process. 22(6), 2398–2408 (2013)

    Article  MathSciNet  Google Scholar 

  2. Arboleda, C., Wang, Z., Stampanoni, M.: Wavelet-based noise-model driven denoising algorithm for differential phase contrast mammography. Opt. Express 21(9), 10572–10589 (2013)

    Article  Google Scholar 

  3. Chen, F., Zhang, L., Yu, H.: External patch prior guided internal clustering for image denoising. In: 2015 IEEE International Conference on Computer Vision, ICCV, pp. 603–611 (2015)

    Google Scholar 

  4. Chen, G., Xie, W., Zhao, Y.: Wavelet-based denoising: a brief review. In: Intelligent Control and Information Processing (ICICIP). pp. 570–574, June 2013

    Google Scholar 

  5. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)

    Article  MathSciNet  Google Scholar 

  6. Domingues Jr., M.O., Mendes, O., da Costa, A.M.: On wavelet techniques in atmospheric sciences. Adv. Space Res. 35(5), 831–842 (2005). Fundamentals of Space Environment Science

    Article  Google Scholar 

  7. Furhad, M.H., Tahtali, M., Lambert, A.: Restoring atmospheric-turbulence-degraded images. Appl. Opt. 55(19), 5082–5090 (2016)

    Article  Google Scholar 

  8. Ghimpeţeanu, G., Batard, T., Bertalmío, M., Levine, S.: Denoising an Image by denoising its components in a moving frame. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2014. LNCS, vol. 8509, pp. 375–383. Springer, Heidelberg (2014). doi:10.1007/978-3-319-07998-1_43

    Google Scholar 

  9. Irum, I., Shahid, M., Sharif, M., Raza, M.: A review of image denoising methods. J. Eng. Sci. Technol. Rev. 8(5), 41–48 (2015)

    Google Scholar 

  10. Ji, Z., Xia, Y., Sun, Q., Xia, D., Feng, D.D.: Local Gaussian distribution fitting based FCM algorithm for brain MR image segmentation. In: Zhang, Y., Zhou, Z.-H., Zhang, C., Li, Y. (eds.) IScIDE 2011. LNCS, vol. 7202, pp. 318–325. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31919-8_41

    Chapter  Google Scholar 

  11. Jiang, J., Zhang, L., Yang, J.: Mixed noise removal by weighted encoding with sparse nonlocal regularization. IEEE Trans. Image Process. 23(6), 2651–2662 (2014)

    Article  MathSciNet  Google Scholar 

  12. Kuijper, A.: Geometrical PDEs based on second-order derivatives of gauge coordinates in image processing. Image Vis. Comput. 27(8), 1023–1034 (2009)

    Article  Google Scholar 

  13. Li, D., Mersereau, R.M., Simske, S.J.: Atmospheric turbulence-degraded image restoration using principal components analysis. IEEE Geosci. Remote Sensing Lett. 4(3), 340–344 (2007)

    Article  Google Scholar 

  14. Li, D., Simske, S.J.: Atmospheric turbulence degraded-image restoration by kurtosis minimization. IEEE Geosci. Remote Sens. Lett. 6(2), 244–247 (2009)

    Article  Google Scholar 

  15. Weickert, J.: Anisotropic Diffusion in Image Processing, vol. 1. Teubner, Stuttgart (1998)

    MATH  Google Scholar 

  16. Niknejad, M., Rabbani, H., Babaie-Zadeh, M.: Image restoration using gaussian mixture models with spatially constrained patch clustering. IEEE Trans. Image Process. 24(11), 3624–3636 (2015)

    Article  MathSciNet  Google Scholar 

  17. Sampat, M.P., Wang, Z., Gupta, S., Bovik, A.C., Markey, M.K.: Complex wavelet structural similarity: a new image similarity index. IEEE Trans. Image Process. 18(11), 2385–2401 (2009)

    Article  MathSciNet  Google Scholar 

  18. Song, C., Ma, K., Li, A., Chen, X., Xu, X.: Diffraction-limited image reconstruction with SURE for atmospheric turbulence removal. Infrared Phys. Technol. 71, 171–174 (2015)

    Article  Google Scholar 

  19. Wang, X., Zhao, X., Guo, F., Ma, J.: Impulsive noise detection by double noise detector and removal using adaptive neural-fuzzy inference system. AEU-Int. J. Electron. Commun. 65(5), 429–434 (2011). Elsevier

    Article  Google Scholar 

  20. Xue, B., Cao, L., Cui, L., Bai, X., Cao, X., Zhou, F.: Analysis of non-Kolmogorov weak turbulence effects on infrared imaging by atmospheric turbulence MTF. Opt. Commun. 300, 114–118 (2013)

    Article  Google Scholar 

  21. Yan, L., Jin, M., Fang, H., Liu, H., Zhang, T.: Atmospheric-turbulence-degraded astronomical image restoration by minimizing second-order central moment. IEEE Geosci. Remote Sens. Lett. 9(4), 672–676 (2012)

    Article  Google Scholar 

  22. Yang, A., Lu, M., Teng, S., Sun, J.: Phase estimation based blind deconvolution for turbulence degraded images. In: 2013 International Conference on Virtual Reality and Visualization (ICVRV), pp. 273–276, September 2013

    Google Scholar 

  23. Zhao, X., Wang, X.: Novel adaptive high-performance and nonlinear filtering algorithm for mixed noise removal. J. Electron. Imaging 21(2), 023005 (2012)

    Article  Google Scholar 

  24. Zhu, X., Milanfar, P.: Removing atmospheric turbulence via space-invariant deconvolution. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 157–170 (2013)

    Article  Google Scholar 

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Acknowledgement

This work supported by Doctor Scientific Research Foundation, Xi’an Polytechnic University, the Special Scientific Research Project of Education Department of Shaanxi Provincial Government (No. 16JK1328), and China Scholarship Council (CSC) Fund.

Author information

Authors and Affiliations

  1. School of Computer Science, Xi’an Polytechnic University, 710048, Xi’an, China

    Xueqing Zhao

  2. Institute for Computer Graphics and Knowledge Visualization, Graz University of Technology, 8010, Graz, Austria

    Xueqing Zhao, Pavlos Mavridis & Tobias Schreck

  3. Fraunhofer IGD, 64283, Darmstadt, Germany

    Arjan Kuijper

  4. Technische Universität Darmstadt, 64289, Darmstadt, Germany

    Arjan Kuijper

Authors
  1. Xueqing Zhao
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  2. Pavlos Mavridis
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  3. Tobias Schreck
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  4. Arjan Kuijper
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Corresponding author

Correspondence to Xueqing Zhao .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

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Zhao, X., Mavridis, P., Schreck, T., Kuijper, A. (2016). RDEPS: A Combined Reaction-Diffusion Equation and Photometric Similarity Filter for Optical Image Restoration. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_5

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  • DOI: https://doi.org/10.1007/978-3-319-50832-0_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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