access icon free Multiple-constraint variational framework and image restoration problems

In this study, an advanced variational model is presented for problem modelling in computer vision and image processing. The proposed model allows for the definition of multiple constraints in data fidelity, which has not been considered in previous state-of-the-art methods. With this definition, the model is more robust and flexible with regard to problem modelling. Two algorithms are introduced to solve the optimisation problems: one for the vector domain and the other for the frequency domain. The issue of multiple L 1-norms in the data fidelity term is resolved with these algorithms; this remained unsolved in previous research because of the difficulty with optimisation. The proposed model is demonstrated through two problems in image processing: image denoising and image deblurring. The results indicate that, compared to previous methods, images of high visual quality were both produced and recovered when using the proposed model. In addition, good and stable results in real-world images were yielded by the proposed model, which indicates vast potential for practical uses.

Inspec keywords: computer vision; image restoration; variational techniques; image denoising; optimisation; frequency-domain analysis

Other keywords: data fldelity; image deblurring; optimisation problem; image restoration problems; image denoising; multiple constraint variational model; problem modelling; visual quality; frequency domain; vector domain; image processing; computer vision; L1-norms

Subjects: Optimisation techniques; Optimisation techniques; Optical, image and video signal processing; Computer vision and image processing techniques

References

    1. 1)
      • 22. Gnu image manipulation program’. Available at: http://www.gimp.org/.
    2. 2)
    3. 3)
    4. 4)
      • 11. Chambolle, A.: ‘An algorithm for total variation minimization and applications’, J. Math. Imaging Vis., 2004, 20, (1–2), pp. 8997.
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
      • 18. Buades, A., Coll, B., Morel, J.M.: ‘A review of image denoising algorithms, with a new one’, Simul, 2005, 4, pp. 490530.
    10. 10)
    11. 11)
      • 17. Buades, A., Coll, B., Morel, J.-M.: ‘A non-local algorithm for image denoising’. CVPR, 2005, (2), pp. 6065.
    12. 12)
      • 12. Pock, T., Unger, M., Cremers, D., Bischof, H.: ‘Fast and exact solution of total variation models on the gpu’. CVPR Workshop on Visual Computer Vision on GPUs, 2008.
    13. 13)
    14. 14)
    15. 15)
    16. 16)
      • 21. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: ‘Image denoising with block-matching and 3d filtering’. Electronic Imaging'06, Proc. SPIE 6064, No. 6064A-30, 2006.
    17. 17)
      • 27. Heide, F., Rouf, M., Hullin, M.B., Labitzke, B., Heidrich, W., Kolb, A.: ‘High-quality computational imaging through simple lenses’, ACM Trans. Graph., 2013, 32, (5), pp. 149:1149:14. Available at: http://www.doi.acm.org/10.1145/2516971.2516974.
    18. 18)
    19. 19)
    20. 20)
      • 15. Estrada, F.J., Fleet, D.J., Jepson, A.D.: ‘Stochastic image denoising’. BMVC, 2009.
    21. 21)
      • 6. Osher, S., Solé, A.F., Vese, L.A.: ‘Image decomposition, image restoration, and texture modeling using total variation minimization and the h/sup-1/ norm’. ICIP, 2003, pp. 689692.
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
    28. 28)
    29. 29)
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