Multimedia Tools and Applications

, Volume 75, Issue 20, pp 12967–12982 | Cite as

Big data driven decision making and multi-prior models collaboration for media restoration

  • Feng Jiang
  • Seungmin Rho
  • Bo-Wei Chen
  • Kun Li
  • Debin Zhao


Aiming at the restoration of degraded social network services media, this paper proposed a novel multi-prior models collaboration framework for image restoration with big data. Different from the traditional non-reference media restoration strategies, a big reference image set is adopted to provide the references and predictions of different popular prior models and accordingly guide the further prior collaboration. With these cues, the collaboration of multi-prior models is mathematically formulated as a ridge regression problem in this paper. Due to the computation complexity of dealing big reference data, scatter-matrix-based KRR is proposed which can achieve high accuracy and low complexity in big data related decision making task. Specifically, an iterative pursuit is proposed to obtain further refined and robust estimation. Five popular prior methods are applied to evaluate the effectiveness of the proposed multi-prior models collaboration. Compared with the traditional restoration strategies, the proposed framework improves the restoration performance significantly and provides a reasonable method for the relative exploration of big data driven decision making.


Prior models Image restoration Big data Data driven 



We would like to thank the authors of [2, 3, 12, 34] and [33] for kindly providing their codes. This work was supported in part by the National Science Council of the Republic of China under Grant No. 103-2917-I-564-058, the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2061978), the National Natural Science Foundation of China under Grant No. 61272386 and 61100096.


  1. 1.
    Afonso MV, Bioucas-Dias JM, Figueiredo MA (2010) Fast image recovery using variable splitting and constrained optimization. IEEE Trans Image Process 19 (9):2345–2356MathSciNetCrossRefGoogle Scholar
  2. 2.
    Aharon M, Elad M, Bruckstein A (2006) K-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54 (11):4311–4322CrossRefGoogle Scholar
  3. 3.
    Beck A, Teboulle M (2009) Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans Image Process 18 (11):2419–2434MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bioucas-Dias JM, Figueiredo MA (2007) A new twist: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Trans Image Process 16 (12):2992–3004MathSciNetCrossRefGoogle Scholar
  5. 5.
    Buades A, Coll B, Morel JM (2006) Image enhancement by non-local reverse heat equation. CMLA Technical Report 22Google Scholar
  6. 6.
    Candès E, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52 (2):489–509MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Chan R, Dong Y, Hintermuller M (2010) An efficient two-phase L1-TV method for restoring blurred images with impulse noise. IEEE Trans Image Process 19 (7):1731–1739MathSciNetCrossRefGoogle Scholar
  8. 8.
    Chen C, Huang J (2012) Compressive sensing mri with wavelet tree sparsity. In: Advances in neural information processing systems, pp 1115–1123Google Scholar
  9. 9.
    Cohen S, Hamilton JT, Turner F (2011) Computational journalism. Commun ACM 54 (10):66–71CrossRefGoogle Scholar
  10. 10.
    Coifman RR, Lafon S, Lee AB, Maggioni M, Nadler B, Warner F, Zucker SW (2005) Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. In: Proceedings of the national academy of sciences, vol 102, pp 7426–7431Google Scholar
  11. 11.
    Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans Image Process 16 (8):2080–2095MathSciNetCrossRefGoogle Scholar
  12. 12.
    Dong W, Zhang L, Shi G, Wu X (2011) Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans Image Process 20 (7):1838–1857MathSciNetCrossRefGoogle Scholar
  13. 13.
    Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52 (4):1289–1306MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Eckstein J, Bertsekas D (1992) On the douglas rachford splitting method and the proximal point algorithm for maximal monotone operators. Math Program 55:293–318MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Efros AA, Leung TK (1999) Texture synthesis by non-parametric sampling. In: ICCV, vol 2, pp 1022–1038Google Scholar
  16. 16.
    Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15 (12):3736–3745MathSciNetCrossRefGoogle Scholar
  17. 17.
    Geman D, Reynolds G (1992) Constrained restoration and the recovery of discontinuitie. IEEE Trans Pattern Anal Mach Intel 14 (3):367–383CrossRefGoogle Scholar
  18. 18.
    Gilboa G, Osher S (2007) Nonlocal operators with applications to image processing. CMLA Technical Report 23Google Scholar
  19. 19.
    Hoerl AE, Kennard RW (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12 (1):55–67Google Scholar
  20. 20.
    James W, Stein C (1961) Estimation with quadratic loss. In: Proceedings of the fourth Berkeley symposium on mathematical statistics and probability, vol 1, pp 361–379Google Scholar
  21. 21.
    Jegou H, Douze M, Schmid C (2008) Inria holidays datasetGoogle Scholar
  22. 22.
    Jung M, Bresson X, Chan TF, Vese LA (2011) Nonlocal mumford-shah regularizers for color image restoration. IEEE Trans Image Process 20 (6):1583–1598MathSciNetCrossRefGoogle Scholar
  23. 23.
    Katkovnik V, Foi A, Egiazarian K, Astola J (2010) From local kernel to nonlocal multiple-model image denoising. Int J Comput Vis 86 (1):1–32MathSciNetCrossRefGoogle Scholar
  24. 24.
    Kindermann S, Osher S, Jones P (2005) Deblurring and denoising of images by nonlocal functionals. Multiscale Model Simul 4 (4):1091–1115MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Lazer D, Pentland AS, Adamic L, Aral S, Barabasi AL, Brewer D, Christakis N, Contractor N, Fowler J, Gutmann M et al (2009) Life in the network: the coming age of computational social science. Science (New York NY) 323 (5915):721CrossRefGoogle Scholar
  26. 26.
    Ma S, Yin W, Zhang Y, Chakraborty A (2008) An efficient algorithm for compressed mr imaging using total variation and wavelets. In: IEEE Conference on computer vision and pattern recognition (CVPR), 2008. IEEE, pp 1–8Google Scholar
  27. 27.
    Maclin R, Opitz D (2011) Popular ensemble methods: an empirical study. arXiv preprint arXiv:
  28. 28.
    Mairal J, Bach F, Ponce J, Sapiro G (2010) Online learning for matrix factorization and sparse coding. J Mach Learn Res 11:19–60MathSciNetMATHGoogle Scholar
  29. 29.
    Mavridis N, Kazmi W, Toulis P (2010) Friends with faces: how social networks can enhance face recognition and vice versa. SpringerGoogle Scholar
  30. 30.
    Mumford D, Shah J (1989) Optimal approximation by piecewise smooth functions and associated variational problems. Commun Pure Applied Math 42:577–685MathSciNetCrossRefMATHGoogle Scholar
  31. 31.
    Olshausen B, Field D (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381:607–609CrossRefGoogle Scholar
  32. 32.
    Portilla J, Strela V, Wainwright MJ, Simoncelli EP (2003) Image denoising using scale mixtures of gaussians in the wavelet domain. IEEE Trans Image Process 12 (11):1338–1351MathSciNetCrossRefMATHGoogle Scholar
  33. 33.
    Roth S, Black MJ (2005) Fields of experts: a framework for learning image priors. In: IEEE computer society conference on computer vision and pattern recognition, vol 2. IEEE, pp 860–867Google Scholar
  34. 34.
    Setzer S (2011) Operator splittings, bregman methods and frame shrinkage in image processing. Int J Comput Vis 92 (3):265–280MathSciNetCrossRefMATHGoogle Scholar
  35. 35.
    Woodbury MA (1950) Inverting modified matrices. Princeton UniversityGoogle Scholar
  36. 36.
    Yin W, Osher S, Goldfarb D, Darbo J (2008) Bregman iterative algorithms for L1-minimization with applications to compressed sensing. SIAM J Imaging Sci 1 (1):142–168CrossRefGoogle Scholar
  37. 37.
    Zhang J, Liu S, Xiong R, Ma S, Zhao D (2013) Improved total variation based image compressive sensing recovery by nonlocal regularization. In: IEEE international symposium on circuits and systems, pp 2836–2839Google Scholar
  38. 38.
    Zhang J, Xiong R, Ma S, Zhao D (2011) High-quality image restoration from partial random samples in spatial domain. In: IEEE conference on visual communications and image processing (VCIP). IEEE, pp 1–4Google Scholar
  39. 39.
    Zhang X, Burger M, Bresson X, Osher S (2010) Bregmanized nonlocal regularization for deconvolution and sparse reconstruction. SIAM J Imaging Sci 3 (3):253–276MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Feng Jiang
    • 1
  • Seungmin Rho
    • 2
  • Bo-Wei Chen
    • 3
  • Kun Li
    • 1
  • Debin Zhao
    • 1
  1. 1.School of Computer ScienceHarbin Institute of TechnologyHarbinChina
  2. 2.Department of MultimediaSungkyul UniversityAnyangKorea
  3. 3.Department of Electrical EngineeringNational Cheng Kung UniversityTainan CityTaiwan

Personalised recommendations