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A DSP-based blind deconvolution algorithm for motion image restoration

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Abstract

In the process of image shooting, image stabilization is an inevitable problem. Therefore, this paper finds an efficient and simple image stabilization algorithm in image stabilization. Using a new image regularization method to reduce the cost of real and clear images, and apply it to the blind deconvolution model without any additional cost. Because of its rapidity and robustness, it matches the processing performance of DSP in image processing. In this article, after re-encoding, so that it can be applied to the DSP, and thus enhance the processing speed of the image processing. In addition, because of the portability of DSP, the DSP embedded with the algorithm can be transplanted to the UAV. The fuzzy picture produced after the shooting is processed in real time to reduce the workload of the later picture processing. Experiments show that the scheme proposed in this paper can achieve real-time performance under the precondition of guaranteeing the computing effect, so as to improve the operating efficiency of the whole system.

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References

  1. Jenkins, S.T., Hilkert, J.M.: Line of sight stabilization using image motion compensation. Acquis. Track. Pointing III 1111, 98–115 (1989)

    Article  Google Scholar 

  2. Li, B., Zhan, Z.H.: Research on motion blurred image restoration. In: IEEE International Congress on Image and Signal Processing, pp. 307–1311 (2012)

  3. Roels, J., Aelterman, J., Vylder, J.D., et al.: Image degradation in microscopic images: avoidance, artifacts, and solutions. In: Focus on Bio-Image Informatics, p. 41. Springer, Berlin (2016)

  4. Zaineldin, H., Elhosseini, M.A., Ali, H.A.: Image compression algorithms in wireless multimedia sensor networks: a survey. Ain Shams Eng. J. 6(2), 481–490 (2015)

    Article  Google Scholar 

  5. Chen, Y., Yu, W., Pock, T.: On learning optimized reaction diffusion processes for effective image restoration. In: IEEE Computer Vision and Pattern Recognition, pp. 5261–5269 (2015)

  6. Mao, X.J., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Proceedings of Advances in Neural Information Processing Systems (2016)

  7. Fan, H., Chen, Y., Guo, Y., et al.: Hyperspectral image restoration using low-rank tensor recovery. In: IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, vol. 99, pp. 1–16 (2017)

  8. Tschumperl, D., Deriche, R.: Constrained and unconstrained PDEs for vector image restoration. In: Scandinavian Conference on Image Analysis (2001)

  9. Khandekar, A., Agrawal, A., Sutivong, A., et al.: Systems and methods that utilize a capacity-based signal-to-noise ratio to predict and improve mobile communication, US, US 7623490 B2 (2009)

  10. Dong, W., Zhang, L., Shi, G., et al.: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. In: IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, vol. 20, no. 7, pp. 1838–1857 (2011)

  11. Dong, W., Zhang, D., Shi, G.: Centralized sparse representation for image restoration. In: International Conference on Computer Vision, pp. 1259–1266. IEEE Computer Society (2011)

  12. Pan. J., Hu, Z., Su, Z., et al.: Deblurring text images via L0-regularized intensity and gradient prior. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2901–2908. IEEE Computer Society (2014)

  13. Lai, W.S., Huang, J.B., Hu, Z., et al.: A comparative study for single image blind deblurring. In: IEEE Computer Vision and Pattern Recognition, pp. 1701–1709 (2016)

  14. Qiu, P., Kang, Y.: Blind image deblurring using jump regression analysis. Stat. Sin. 25(3), 879–899 (2015)

    MathSciNet  MATH  Google Scholar 

  15. Pan, J., Sun, D., Pfister, H., et al.: Blind image deblurring using dark channel prior. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1628–1636. IEEE Computer Society (2016)

  16. Mahmoud, M.: The removal of motion misalignment errors and noise blurring in MRI images. In: Iasted International Conference on Modelling and Simulation, pp. 216–221 (2003)

  17. Fergus, R., Singh, B., Hertzmann, A., et al.: Removing camera shake from a single photograph. In: ACM SIGGRAPH, pp. 787–794. ACM (2006)

  18. Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graph. 27(3), 1–10 (2008)

    Article  Google Scholar 

  19. Xu, L., Jia, J.: Two-phase kernel estimation for robust motion deblurring. In: Proceedings of Computer Vision—ECCV 2010, European Conference on Computer Vision, Heraklion, Crete, Greece, September 5–11, DBLP, pp. 157–170 (2010)

  20. Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. 28(5), 1–8 (2009)

    Article  Google Scholar 

  21. Krishnan, D, Tay, T., Fergus, R.: Blind deconvolution using a normalized sparsity measure. In: IEEE Computer Vision and Pattern Recognition, pp. 233–240 (2011)

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Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xi’an, 710127, China

    Jian Zhao, Yuan Shi & Shunli Zhang

  2. School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Duan Xie

Authors
  1. Jian Zhao
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  2. Yuan Shi
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  3. Shunli Zhang
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  4. Duan Xie
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Correspondence to Shunli Zhang.

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Zhao, J., Shi, Y., Zhang, S. et al. A DSP-based blind deconvolution algorithm for motion image restoration. Cluster Comput 22 (Suppl 4), 8493–8500 (2019). https://doi.org/10.1007/s10586-018-1881-0

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  • DOI: https://doi.org/10.1007/s10586-018-1881-0

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