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2016 | OriginalPaper | Buchkapitel

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

verfasst von : Xueqing Zhao, Pavlos Mavridis, Tobias Schreck, Arjan Kuijper

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

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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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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)MathSciNetCrossRef

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)CrossRef

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)

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

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)MathSciNetCrossRef

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 ScienceCrossRef

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

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

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

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 CrossRef

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)MathSciNetCrossRef

12.

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

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)CrossRef

14.

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

15.

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

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)MathSciNetCrossRef

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)MathSciNetCrossRef

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)CrossRef

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). ElsevierCrossRef

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)CrossRef

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)CrossRef

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

23.

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

24.

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

Titel: RDEPS: A Combined Reaction-Diffusion Equation and Photometric Similarity Filter for Optical Image Restoration
verfasst von: Xueqing Zhao
Pavlos Mavridis
Tobias Schreck
Arjan Kuijper
Verlag: Springer International Publishing
Buch: Advances in Visual Computing
Print ISBN: 978-3-319-50831-3

Electronic ISBN: 978-3-319-50832-0

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-50832-0_5

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