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A generalized modeling of ill-posed inverse reconstruction of images using a novel data-driven framework

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Abstract

By definition, an instance of image reconstruction often combines denoising, deblurring and enhancing objectively and appears as a fundamental process in visual processing systems which is mathematically an ill-posed inverse optimization problem. It suffers additional complexities because of a non-stationary nature of the image, restricting available methods to produce inconsistent restoration on a variety of image and degradation types. It requires filling this gap by a generic framework of image restoration. Therefore, in this paper, we modeled a novel data-driven framework of generic image reconstruction as a testing benchmark to the emerging application of deep learning. The proposed framework comprises of data engineering, image filtering and a regression neural network for better restoration of many degraded images. We evaluated the effectiveness of the framework on many images degraded by a Gaussian, out-of-focus, motion or airy-pattern blur and random additive white Gaussian noise. The performance appeared better in comparison to a benchmark and state-of-the-art generic image restoration results on several indicators. Furthermore, the framework performed better in comparison to a recent approach to image reconstruction which uses a deep convolutional neural network.

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Notes

  1. Dataset website: http://archive.stsci.edu/cgi-bin/dss_form.

References

  1. Marcus, G.: Deep Learning: A Critical Appraisal (Jan 2018). arXiv:1801.00631v1 [cs.AI]

  2. Flamary, R.: Astronomical image reconstruction with convolutional neural networks. In: 25th European Signal Processing Conference (EUSIPCO), Kos, pp. 2468-2472. (2017). https://doi.org/10.23919/EUSIPCO.2017.8081654

  3. Jin, K.H., McCann, M.T., Froustey, E., Unser, M.: Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process. 26(9), 4509–4522 (2017)

    Article  MathSciNet  Google Scholar 

  4. Biggs, D.S., Andrews, M.: Acceleration of iterative image restoration algorithms. Appl. Opt. 36(8), 1766–1775 (1997)

    Article  Google Scholar 

  5. Hong, H., Hua, X., Zhang, X., et al.: Multi-frame real image restoration based on double loops with alternative maximum likelihood estimation. SIViP 10, 1489 (2016). https://doi.org/10.1007/s11760-016-0960-z

    Article  Google Scholar 

  6. Hansen, P.C. et al.: Deblurring Images: Matrices, Spectra, and Filtering. Society of Industrial Appllied Mathematics, Philadelphia, PA 19104-2688 USA (2006)

  7. Bilal, M., et al.: Optimal edge preserving restoration with efficient regularisation. Imaging Sci. J. 63(2), 68–75 (2015)

    Article  Google Scholar 

  8. Kheradmand, A., Milanfar, P.: A general framework for regularized, similarity-based image restoration. IEEE Trans. Image Process. 24, 5136–5151 (2014)

    Article  MathSciNet  Google Scholar 

  9. Bilal, M., et al.: Novel optimization framework to recover true image data. Cognit. Comput. 7(6), 680–692 (2015)

    Article  Google Scholar 

  10. Bilal, M., et al.: Modified particle swarm optimization and fuzzy regularization for pseudo de-convolution of spatially variant blurs. Multimed. Tools Appl. 75, 6533–6548 (2015). https://doi.org/10.1007/s11042-015-2587-4

    Article  Google Scholar 

  11. Bilal, M., Wyne, M.F., Jaffar, M.A.: Image restoration by multivariate-stochastic optimization using improved particle swarm algorithm. In: Proceedings IEEE Congress Evolutionary Computation (CEC), pp. 2596–2603 (2016). https://doi.org/10.1007/s11042-015-2587-4

  12. Perry, S.W.: Adaptive image restoration: perception based neural network models and algorithms, Ph.D. dissertation, School of Electrical and Information Engineering, Univ. Sydney, NSW, Australia (2006)

  13. Xu, L., Ren, J., Liu, C., Jia, J.: Deep convolutional neural network for image deconvolution. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger,K.Q. (Eds.) Proceedings of the 27th International Conference on Neural Information Processing Systems, Volume 1 (NIPS’14), Vol. 1., pp. 1790-1798. MIT Press, Cambridge, MA, USA (Dec. 2014)

  14. Pires, R., Santos, D., Souza, G., Levada, A., Papa, J.: A deep boltzmann machine-based approach for robust image denoising. In: 22nd Iberoamerican Congress on Pattern Recognition, At Santiago, Chile (Nov. 2017)

  15. Gonzalez, R.C., Woods, R.: Digital Image Processing, 3rd edn. Prentice Hall, Upper Sadle River (2008)

    Google Scholar 

  16. Sharif, M., Hussain, A., Jaffar, M.A., et al.: Fuzzy-based hybrid filter for Rician noise removal. SIViP 10, 215 (2016). https://doi.org/10.1007/s11760-014-0729-1

    Article  Google Scholar 

  17. Liu, L., Philip Chen, C.L., Zhou, Y., et al.: A new weighted mean filter with a two-phase detector for removing impulse noise. Inf. Sci 315, 1–16 (2015). https://doi.org/10.1016/j.ins.2015.03.067

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Gonzalez, R.C., et al.: Digital Image Processing Using MATLAB, 2nd edn. Gatesmark Publishing, Knoxville (2009)

    Google Scholar 

  21. Mignotte, M.: A segmentation based regularization term for image deconvolution. IEEE Trans. Image Process. 15, 1973–1984 (2006)

    Article  Google Scholar 

  22. Siadat, M., Aghazadeh, N., Öktem, O.: Reordering for improving global Arnoldi–Tikhonov method in image restoration problems. SIViP 12, 497 (2018). https://doi.org/10.1007/s11760-017-1185-5

    Article  Google Scholar 

  23. Adam, T., Paramesran, R.: Hybrid non-convex second-order total variation with applications to non-blind image deblurring. SIViP (2019). https://doi.org/10.1007/s11760-019-01531-3

    Article  MATH  Google Scholar 

  24. Paik, J., Katsaggelos, A.: Image restoration using a modified hopfield network. IEEE Trans Image Process. 1(1), 49–63 (1992)

    Article  Google Scholar 

  25. Bilal, M., et al.: Estimation and optimization based ill-posed inverse restoration using fuzzy logic. Multimed Tools Appl. 69(3), 1067–1087 (2014)

    Article  Google Scholar 

  26. Huang, C., et al.: Robust image restoration via adaptive low-rank approximation and joint kernel regression. IEEE Trans. Image Process. 23, 5284–97 (2014)

    Article  MathSciNet  Google Scholar 

  27. Liu, J., et al.: An efficient variational method for image restoration. Abstr Appl Anal 213536, 1–11 (2013)

    MathSciNet  Google Scholar 

  28. Le, J., et al.: A new efficient alternating method for image restoration and texture extraction. J. Comput. Inf. Syst. 9(7), 2595–2602 (2013)

    Google Scholar 

  29. Zhang, X., et al.: Non-blind deblurring of structured images with geometric deformation. Vis Comput. 3(2), 131–140 (2015)

    Article  Google Scholar 

  30. Huang, H., Wang, K.: Texture-preserving deconvolution via image decomposition. SIViP 11, 1189 (2017). https://doi.org/10.1007/s11760-017-1074-y

    Article  Google Scholar 

  31. Ren, W. et al.: Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation, 32 Neural Information Processing Systems (NIPS 2018), Montreal, Canada, pp. 297–307 (2018)

  32. Bilal, M., Rehman, MSu, Jaffar, M.A.: Evolutionary reconstruction: image restoration for space variant degradation. Smart Comput. Rev 3(4), 220–232 (2013)

    Article  Google Scholar 

  33. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings 8th International Conference Computer Vision, vol. 2, pp. 416–423 (July 2001)

  34. Roth, S., Black, M.J.: Fields of experts. Int. J. Comput. Vis. 82(2), 205–229 (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, College of Computers and Information Systems, Umm Al-Qura University, Mecca, Saudi Arabia

    Mohsin Bilal & Muhammad Arif

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  1. Mohsin Bilal
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  2. Muhammad Arif
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Correspondence to Mohsin Bilal.

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Bilal, M., Arif, M. A generalized modeling of ill-posed inverse reconstruction of images using a novel data-driven framework. SIViP 14, 333–341 (2020). https://doi.org/10.1007/s11760-019-01559-5

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  • DOI: https://doi.org/10.1007/s11760-019-01559-5

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