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Adaptive regularised l 2-boosting on clustered sparse coefficients for single image super-resolution

Adaptive regularised l 2-boosting on clustered sparse coefficients for single image super-resolution

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In this study, the authors propose a novel approach for single image super-resolution. Their method is based on the idea of learning a mapping function, which can reveal the intrinsic relationship between sparse coefficients of low-resolution (LR) and high-resolution (HR) image patch pairs with respect to their individual dictionaries. Adaptive regularised l 2-boosting algorithm is proposed to learn this type of mapping function. Specifically, to reduce time consumption, the authors cluster training patches into several clusters. Within each cluster, a pair of dictionaries for LR and HR image patches is jointly trained. Adaptive regularised l 2-boosting algorithm is then employed to obtain the function. Thus, in a reconstruction stage, for each given input LR image patch, the authors can effectively estimate its corresponding HR image patch. Their extensive experimental results demonstrated that the proposed method achieves a performance of similar quality performance to that of the top methods.

References

    1. 1)
      • X. Li , M.T. Orchard .
        1. Li, X., Orchard, M.T.: ‘New edge-directed interpolation’, IEEE Trans. Image Process., 2001, 10, (10), pp. 15211527.
        . IEEE Trans. Image Process. , 10 , 1521 - 1527
    2. 2)
      • X. Li , H. He , Z. Yin .
        2. Li, X., He, H., Yin, Z., et al: ‘KPLS-based image super-resolution using clustering and weighted boosting’, Neurocomputing, 2015, 149, (PB), pp. 940948.
        . Neurocomputing , 940 - 948
    3. 3)
      • J. Oliveira , J. Bioucas , M. Figueiredo .
        3. Oliveira, J., Bioucas, J., Figueiredo, M.: ‘Adaptive total variation image deblurring: amajorization-minimization approach’, Signal Process., 2009, 89, (9), pp. 16831693.
        . Signal Process. , 9 , 1683 - 1693
    4. 4)
      • W. Dong , L. Zhang .
        4. Dong, W., Zhang, L.: ‘Sparse representation based image interpolation with nonlocal autoregressive modeling’, IEEE Trans. Image Process., 2013, 4, (22), pp. 13821394.
        . IEEE Trans. Image Process. , 22 , 1382 - 1394
    5. 5)
      • J. Zhang , D. Zhao , W. Gao .
        5. Zhang, J., Zhao, D., Gao, W.: ‘Group-based sparse representation for image restoration’, IEEE Trans. Image Process., 2014, 23, (8), pp. 33363351.
        . IEEE Trans. Image Process. , 8 , 3336 - 3351
    6. 6)
      • Z. Lin , H. Shum .
        6. Lin, Z., Shum, H.: ‘Fundamental limits of reconstruction-based super-resolution algorithms under local translation’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (1), pp. 8397.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 1 , 83 - 97
    7. 7)
      • F. Zhou , W. Yang , Q. Liao .
        7. Zhou, F., Yang, W., Liao, Q.: ‘Single image super-resolution using incoherent sub-dictionaries learning’, IEEE Trans. Consum. Electron., 2012, 58, (3), pp. 891897.
        . IEEE Trans. Consum. Electron. , 3 , 891 - 897
    8. 8)
      • W.T. Freeman , T.R. Jones , E.C. Pasztor .
        8. Freeman, W.T., Jones, T.R., Pasztor, E.C.: ‘Example-based superresolution’, IEEE Comput. Graph., 2002, 22, (2), pp. 5665.
        . IEEE Comput. Graph. , 2 , 56 - 65
    9. 9)
      • S. Tang , L. Xiao , P. Liu .
        9. Tang, S., Xiao, L., Liu, P., et al: ‘Partial least-squares regression on common feature space for single image superresolution’, J. Electron. Imaging, 2014, 23, (5), p. 053006.
        . J. Electron. Imaging , 5 , 053006
    10. 10)
      • K.S. Ni , T.Q. Nguyen .
        10. Ni, K.S., Nguyen, T.Q.: ‘Image superresolution using support vector regression’, IEEE Trans. Image Process., 2007, 16, (6), pp. 15961610.
        . IEEE Trans. Image Process. , 6 , 1596 - 1610
    11. 11)
      • C. Dong , C. Loy , K. He .
        11. Dong, C., Loy, C., He, K., et al: ‘Learning a deep convolutional network for image super-resolution’. Proc. European Conf. on Computer Vision, Zurich, Switzerland, June 2014, pp. 184199.
        . Proc. European Conf. on Computer Vision , 184 - 199
    12. 12)
      • D. Dai , R. Timofte , L. Gool .
        12. Dai, D., Timofte, R., Gool, L.: ‘Jointly optimized regressors for image super-resolution’, Image Video Process., 2015, 34, (2), pp. 95104.
        . Image Video Process. , 2 , 95 - 104
    13. 13)
      • H. Chang , D.Y. Yeung , Y. Xiong .
        13. Chang, H., Yeung, D.Y., Xiong, Y.: ‘Super-resolution through neighbor embedding’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Washington, USA, July 2004, pp. 275282.
        . Proc. IEEE Conf. Computer Vision and Pattern Recognition , 275 - 282
    14. 14)
      • X. Gao , K. Zhang , X. Li .
        14. Gao, X., Zhang, K., Li, X., et al: ‘Image super-resolution with sparse neighbor embedding’, IEEE Trans. Image Process., 2012, 21, (7), pp. 31943205.
        . IEEE Trans. Image Process. , 7 , 3194 - 3205
    15. 15)
      • J. Yang , J. Write , T. Huang .
        15. Yang, J., Write, J., Huang, T., et al: ‘Image super-resolution via sparse representation’, IEEE Trans. Image Process., 2010, 19, (11), pp. 8612873.
        . IEEE Trans. Image Process. , 11 , 861 - 2873
    16. 16)
      • R. Zeyde , M. Elad , M. Protter .
        16. Zeyde, R., Elad, M., Protter, M.: ‘On single image scale-up using sparse-representations’. Proc. 7th Int. Conf. Curves and Surfaces, Avignon, France, June 2012, pp. 711730.
        . Proc. 7th Int. Conf. Curves and Surfaces , 711 - 730
    17. 17)
      • S. Wang , L. Zhang , Y. Liang .
        17. Wang, S., Zhang, L., Liang, Y., et al: ‘Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch image synthesis’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Providence, USA, June 2012, pp. 22162223.
        . Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2216 - 2223
    18. 18)
      • R. Walha , F. Drira , F. Lebourgeois .
        18. Walha, R., Drira, F., Lebourgeois, F., et al: ‘Resolution enhancement of textual images via multiple coupled dictionaries and adaptive sparse representation selection’, Int. J. Doc. Anal. Recognit., 2015, 18, (1), pp. 87107.
        . Int. J. Doc. Anal. Recognit. , 1 , 87 - 107
    19. 19)
      • Y. Tang , Y. Yuan , P. Yang .
        19. Tang, Y., Yuan, Y., Yang, P., et al: ‘Greedy regression in sparse coding space for single-image super-resolution’, J. Vis. Commun. Image Represent., 2013, 24, (2), pp. 148159.
        . J. Vis. Commun. Image Represent. , 2 , 148 - 159
    20. 20)
      • K. Zhang , D. Tao , X. Gao .
        20. Zhang, K., Tao, D., Gao, X.: ‘Learning multiple linear mappings for efficient single image super-resolution’, IEEE Trans. Image Process., 2015, 24, (3), pp. 846861.
        . IEEE Trans. Image Process. , 3 , 846 - 861
    21. 21)
      • F. Zhou , T. Yuan , W. Yang .
        21. Zhou, F., Yuan, T., Yang, W., et al: ‘Single-image super-resolution based on compact KPCA coding and kernel regression’, IEEE Signal Process. Lett., 2015, 22, (3), pp. 336340.
        . IEEE Signal Process. Lett. , 3 , 336 - 340
    22. 22)
      • R. Timofte , V. Smet , L. Gool .
        22. Timofte, R., Smet, V., Gool, L.: ‘Anchored neighborhood regression for fast example-based super-resolution’. Proc. IEEE Int. Conf. on Computer Vision, Sydney, Australia, January 2013, pp. 19201927.
        . Proc. IEEE Int. Conf. on Computer Vision , 1920 - 1927
    23. 23)
      • R. Timofte , V. Smet , L. Gool .
        23. Timofte, R., Smet, V., Gool, L.: ‘A+: adjusted anchored neighborhood regression for fast super-resolution’. Proc. Asian Conf. on Computer Vision, Singapore, November 2014, pp. 111126.
        . Proc. Asian Conf. on Computer Vision , 111 - 126
    24. 24)
      • D. Trinh , M. Luong , F. Dibos .
        24. Trinh, D., Luong, M., Dibos, F., et al: ‘Novel example-based method for super-resolution and denoising of medical images’, IEEE Trans. Image Process., 2014, 23, (4), pp. 18821893.
        . IEEE Trans. Image Process. , 4 , 1882 - 1893
    25. 25)
      • M. Aharon , M. Elad , A. Bruckstein .
        25. Aharon, M., Elad, M., Bruckstein, A.: ‘K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation’, IEEE Trans. Signal Process., 2006, 54, (11), pp. 43114322.
        . IEEE Trans. Signal Process. , 11 , 4311 - 4322
    26. 26)
      • R. Timofte , R. Rothe , L. Gool .
        26. Timofte, R., Rothe, R., Gool, L.: ‘Seven ways to improve example-based single image super resolution’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, USA, June 2016, pp. 17.
        . Proc. IEEE Conf. Computer Vision and Pattern Recognition , 1 - 7
    27. 27)
      • J. Huang , A. Singh , N. Ahuja .
        27. Huang, J., Singh, A., Ahuja, N.: ‘Single image super-resolution from transformed self-exemplars’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Boston, USA, June 2015, pp. 51975206.
        . Proc. IEEE Conf. Computer Vision and Pattern Recognition , 5197 - 5206
    28. 28)
      • J. Yang , Z. Wang , Z. Lin .
        28. Yang, J., Wang, Z., Lin, Z., et al: ‘Coupled dictionary training for image super-resolution’, IEEE Trans. Image Process., 2012, 21, (8), pp. 34673478.
        . IEEE Trans. Image Process. , 8 , 3467 - 3478
    29. 29)
      • S. Yang , M. Wang , Y. Chen .
        29. Yang, S., Wang, M., Chen, Y., et al: ‘Single-image super-resolution reconstruction via learned geometric dictionaries and clustered sparse coding’, IEEE Trans. Image Process., 2012, 21, (9), pp. 40164028.
        . IEEE Trans. Image Process. , 9 , 4016 - 4028
    30. 30)
      • Y. Sun , G. Gu , X. Sui .
        30. Sun, Y., Gu, G., Sui, X., et al: ‘Compressive superresolution imaging based on local and nonlocal regularizations’, IEEE Photonics J., 2016, 8, (1), p. 6900112.
        . IEEE Photonics J. , 1 , 6900112
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