• Infrared and Laser Engineering
  • Vol. 47, Issue 11, 1126001 (2018)
Xue Juntao*, Ni Chenyang, and Yang Sixue
Author Affiliations
  • [in Chinese]
  • show less
    DOI: 10.3788/irla201847.1126001 Cite this Article
    Xue Juntao, Ni Chenyang, Yang Sixue. Image inpainting based on feature clustering and locality-sensitive sparse representation[J]. Infrared and Laser Engineering, 2018, 47(11): 1126001 Copy Citation Text show less
    References

    [1] Elad M, Figueiredo M A T, Ma Y. On the role of sparse and redundant representations in image processing[J]. Proceedings of the IEEE, 2010, 98(6): 972-982.

    [2] Michal Aharon, Michael Elad, Alfred Bruckstein. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.

    [3] Beckouche J, Starck J L, Fadili J. Astronomical image denoising using dictionary learning[J]. Astronomy & Astrophysics, 2013, 556(7): 132.

    [4] Hu Gaolong, Xiong Ling. Criminisi-based sparse representation for image inpainting [C]//2017 IEEE Third International Conference on Multimedia Big Data, 2017: 389-393.

    [5] Huang Linjiang, Liu Hong, Tao Shaojie. An improved inpainting algorithm based on K-SVD dictionary[J]. Journal of Anhui University(Natural Science Edition), 2013, 37(3): 69-74. (in Chinese)

    [6] Han Yulan, Zhao Yongping, Wang Qisong, et al. Reconstruction of super resolution for noise image under the sparse representation[J]. Optics and Precision Engineering, 2017, 25(6): 1619-1626. (in Chinese)

    [7] Xu Kai, Wang Nannan, Gao Xinbo. Image inpainting based on sparse representation with dictionary pre-clustering[C]//7th Chinese Conference on Pattern Recognition, 2016: 245-258.

    [8] Mairal Julien, Bach Francis, Ponce Jean, et al. Online dictionary learning for sparse coding[C]//26th Annual International Conference on Machine Learning, 2009: 689-696.

    [9] Naderahmadian Yashar, Beheshti Soosan, Tinati Mohammad Ali. Correlation based online dictionary learning algorithm[J]. IEEE Transactions on Signal Processing, 2015, 64(3): 592-602.

    [10] Marquez M A, Mojica E, Arguello H. Data sinogram sparse reconstruction based on steering kernel regression and filtering strategies[C]//SPIE, 2016, 9847: 98470Z.

    [11] Sahoo S K, Makur A. Replacing K-SVD with SGK: Dictionary training for sparse representation of images[C]//2015 IEEE International Conference on Digital Signal Processing, 2015(2): 614-617.

    [12] Verhack R, Sikora T, Lange, L, et al. A universal image coding approach using sparse steered Mixture-of-Experts regression[C]//2016 IEEE International Conference on Image Processing, 2016: 2142-2146.

    [13] Marquez M A, Mojica E, Arguello H. Data sinogram sparse reconstruction based on steering kernel regression and filtering strategies[C]//SPIE, 2016, 9847: 98470Z.

    [14] Zhang Kaibing, Gao Xinbo. Single image super-resolution with non-local means and steering Kernel regression[J]. IEEE Transactions on Image Processing, 2012, 21(11): 4544-4556.

    [15] Wei Chiapo, Chao Yuwei, Yeh Yiren, et al. Locality-sensitive dictionary learning for sparse representation based classification[J]. Pattern, 2013, 46(5): 1277-1287.

    [16] Guo Jun, Guo Yangqing, Li Yi, et al. Locality sensitive discriminative dictionary learning[C]//IEEE International Conference on Image Processing, 2015: 1558-1562.

    [17] Rao T J V S, Rao M V G, Aswini T V N L. Image inpainting with group based sparse representation using self adaptive dictionary learning[C]//International Conference on Signal Processing & Communication Engineering Systems, 2015: 301-305.

    [18] Zhang Lin, Zhang Lei, Mou Xuanqin, et al. FSIM: a feature similarity index for image qualtiy assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8): 2378-2386.

    Xue Juntao, Ni Chenyang, Yang Sixue. Image inpainting based on feature clustering and locality-sensitive sparse representation[J]. Infrared and Laser Engineering, 2018, 47(11): 1126001
    Download Citation