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Image Restoration in Portable Devices: Algorithms and Optimization

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Abstract

Image and video data acquired by portable devices such as mobile phones are degraded by noise and blur due to the small size of optical sensors in these devices. A wide range of image restoration methods exists, yet feasibility of these methods in portable platforms is not guaranteed due to limited hardware resources on such platforms. The paper addresses this problem by focusing on denoising algorithms. We have chosen two representatives of denoising methods with state-of-the-art performance, and propose different parallel implementations and algorithmic simplifications suitable for mobile phones. In addition, an extension to resolution enhancement is presented including both visual and quantitative comparisons. Analysis of the algorithms is carried out with respect to the computation time, power consumption and output image quality.

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References

  1. Bordallo López, M., Nieto, A., Boutellier, J., Hannuksela, J., Silvén, O. (2014). Evaluation of real-time lbp computing in multiple architectures. Journal of Real-Time Image Processing, 1–22.

  2. Buades, A., & Coll, B. (2005). A non-local algorithm for image denoising. In Computer vision and pattern recognition (CVPR) (pp. 60–65).

  3. Buckler, M., Jayasuriya, S., Sampson, A. (2017). Reconfiguring the imaging pipeline for computer vision. In IEEE International conference on computer vision (ICCV) (pp. 975–984).

  4. Burger, H.C., Schuler, C.J., Harmeling, S. (2012). Image denoising: can plain neural networks compete with BM3D? In Computer vision and pattern recognition (CVPR) (pp. 2392–2399).

  5. Chatterjee, P., & Milanfar, P. (2010). Is denoising dead? IEEE Transactions on Image Processing, 19(4), 895–911.

    Article  MathSciNet  Google Scholar 

  6. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K. (2007). Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on Image Processing, 16(8), 2080–2095.

    Article  MathSciNet  Google Scholar 

  7. Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 15(12), 3736–3745. https://doi.org/10.1109/TIP.2006.881969.

    Article  MathSciNet  Google Scholar 

  8. Farsiu, S., Robinson, M., Elad, M., Milanfar, P. (2004). Fast and robust multiframe super resolution. IEEE Transactions on Image Processing, 13(10), 1327–1344.

    Article  Google Scholar 

  9. Fortun, D., Bouthemy, P., Kervrann, C. (2015). Optical flow modeling and computation: a survey. Computer Vision and Image Understanding, 134, 1–21.

    Article  Google Scholar 

  10. Goossens, B., Luong, H., Aelterman, J., Pižurica, A., Philips, W. (2010). A GPU-accelerated real-time NLMeans algorithm for denoising color video sequences, (pp. 46–57). Berlin: Springer.

    Google Scholar 

  11. Grasso, I., Radojkovic, P., Rajovic, N., Gelado, I., Ramirez, A. (2014). Energy efficient hpc on embedded socs: optimization techniques for Mali gpu. In 2014 IEEE 28th International parallel and distributed processing symposium (pp. 123–132).

  12. Hannuksela, J., Niskanen, M., Turtinen, M. (2015). Performance evaluation of image noise reduction computing on a mobile platform. In 2015 International conference on embedded computer systems: architectures, modeling, and simulation (SAMOS) (pp. 332–337).

  13. Lebrun, M., Buades, A., Morel, J.M. (2013). A nonlocal Bayesian image denoising algorithm. SIAM Journal on Imaging Sciences, 6(3), 1665–1688.

    Article  MathSciNet  Google Scholar 

  14. Levin, A., & Nadler, B. (2011). Natural image denoising: optimality and inherent bounds. In Computer vision and pattern recognition (CVPR) (pp. 2833–2840).

  15. Liu, C. (2009). Beyond pixels: exploring new representations and applications for motion analysis. Ph.D. thesis, Massachusetts Institute of Technology.

  16. Márques, A., & Pardo, A. (2013). Implementation of non local means filter in GPUs, (pp. 407–414). Berlin: Springer.

    Google Scholar 

  17. Palma, G., Comerci, M., Alfano, B., Cuomo, S., Michele, P.D., Piccialli, F., Borrelli, P. (2013). 3d non-local means denoising via multi-gpu. In 2013 Federated conference on computer science and information systems (pp. 495–498).

  18. Šroubek, F., Cristobal, G., Flusser, J. (2007). A unified approach to superresolution and multichannel blind deconvolution. IEEE Transactions on Image Processing, 16(9), 2322–2332.

    Article  MathSciNet  Google Scholar 

  19. Šroubek, F., Flusser, J., Cristóbal, G. (2009). Super-resolution and blind deconvolution for rational factors with an application to color images. The Computer Journal, 52, 142–152.

    Article  Google Scholar 

  20. Zitová, B., & Flusser, J. (2003). Image registration methods: a survey. Image and Vision Computing, 21, 977–1000.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by ARTEMIS JU project 621439 (ALMARVI) and partially also by the Czech Science Foundation project GA18-05360S.

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Authors and Affiliations

  1. Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou věží, CZ-182 08, Prague 8, Czech Republic

    Jan Kamenický, Filip Šroubek & Barbara Zitová

  2. Visidon Ltd, Teknologiantie 2, 90590, Oulu, Finland

    Jari Hannuksela & Markus Turtinen

Authors
  1. Jan Kamenický
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  2. Filip Šroubek
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  3. Barbara Zitová
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  4. Jari Hannuksela
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  5. Markus Turtinen
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Correspondence to Jan Kamenický.

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Kamenický, J., Šroubek, F., Zitová, B. et al. Image Restoration in Portable Devices: Algorithms and Optimization. J Sign Process Syst 91, 9–20 (2019). https://doi.org/10.1007/s11265-018-1410-7

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  • DOI: https://doi.org/10.1007/s11265-018-1410-7

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