access icon free Single-image super resolution using evolutionary sparse coding technique

Sparse coding (SC) has recently become a widely used tool in signal and image processing. The sparse linear combination of elements from an appropriately chosen over-complete dictionary can represent many signal patches. SC applications have been explored in many fields such as image super resolution (SR), image-feature extraction, image reconstruction, and segmentation. In most of these applications, learning-based SC has provided an excellent image quality. SC involves two steps: dictionary construction and searching the dictionary using quadratic programming. This study focuses on the searching step and a new adaptive variation of genetic algorithm is proposed to search and find the optimum closest match in the dictionary. Also, inspired by the proposed evolutionary SC (ESC), a single-image SR algorithm is proposed. A sparse representation for each patch of the low-resolution input image is obtained by ESC and it is used to generate the high-resolution output image. Experimental results show that the proposed ESC-based method would lead to a better SR image quality.

Inspec keywords: search problems; image coding; genetic algorithms; image resolution; quadratic programming; learning (artificial intelligence)

Other keywords: learning-based SC; signal patch representation; single-image SR algorithm; dictionary search; image quality; optimum closest match; adaptive variation; over-complete dictionary; sparse linear combination; low-resolution input image patch; sparse representation; evolutionary SC technique; high-resolution output image generation; genetic algorithm; quadratic programming; ESC; dictionary construction; sparse coding; image processing

Subjects: Knowledge engineering techniques; Optimisation techniques; Image and video coding; Optimisation techniques; Computer vision and image processing techniques

References

    1. 1)
      • 22. Dong, W., Zhang, L., Lukac, R., et al: ‘Sparse representation based image interpolation with nonlocal autoregressive modeling’, IEEE Trans. Image Process., 2013, 22, (4), pp. 13821394.
    2. 2)
      • 11. Bahy, R.M., Salama, G.I., Mahmoud, T.A.: ‘Adaptive regularization-based super resolution reconstruction technique for multi-focus low-resolution images’, Signal Process., 2014, 103, pp. 155167.
    3. 3)
      • 12. Yang, J., Wright, J., Huang, T.S., et al: ‘Image super-resolution via sparse representation’, IEEE Trans. Image Process., 2010, 19, (11), pp. 28612873.
    4. 4)
      • 10. Li, X., Hu, Y., Gao, X., et al: ‘A multi-frame image super-resolution method’, Signal Process., 2010, 90, (2), pp. 405414.
    5. 5)
      • 7. Zhang, K., Mu, G., Yuan, Y., et al: ‘Video super-resolution with 3D adaptive normalized convolution’, Neurocomputing, 2012, 94, pp. 140151.
    6. 6)
      • 17. Pati, Y.C., Rezaiifar, R., Krishnaprasad, P.S.: ‘Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition’. Proc. of 27th Asilomar Conf. on Signals, Systems and Computers, 1993, pp. 4044.
    7. 7)
      • 25. Wang, L., Geng, H., Liu, P., et al: ‘Particle swarm optimization based dictionary learning for remote sensing big data’, Knowl.-Based Syst., 2015, 79, pp. 4350.
    8. 8)
      • 16. Zeyde, R., Elad, M., Protter, M.: ‘On single image scale-up using sparse-representations’. Int. Conf. on Curves and Surfaces, Springer, Berlin, Heidelberg.
    9. 9)
      • 27. Lee, H., Battle, A., Raina, R., et al: ‘Efficient sparse coding algorithms’. Advances in Neural Information Processing Systems, 2006, pp. 801808.
    10. 10)
      • 3. Feng, W., Lei, H.: ‘Single-image super-resolution with total generalised variation and Shearlet regularisations’, Image Process. IET, 2014, 8, (12), pp. 833845.
    11. 11)
      • 31. Zeng, W., Lu, X., Fei, S.: ‘Image super-resolution employing a spatial adaptive prior model’, Neurocomputing, 2015, 162, pp. 218233.
    12. 12)
      • 8. Zhang, X., Lam, E.Y., Wu, E.X., et al: ‘Application of Tikhonov regularization to super-resolution reconstruction of brain MRI images’. Int. Conf. on Medical Imaging and Informatics (MIMI2007), Empark Grand Hotel Beijing, China, 14–16 August, 2007.
    13. 13)
      • 9. Farsiu, S., Robinson, M.D., Elad, M., et al: ‘Fast and robust multiframe super resolution’, IEEE Trans. Image Process., 2004, 13, (10), pp. 13271344.
    14. 14)
      • 23. Tang, Y., Chen, H., Liu, Z., et al: ‘Example-based super-resolution via social images’, Neurocomputing, 2016, 172, pp. 3847.
    15. 15)
      • 30. Wu, Q.H., Cao, Y.J., Wen, J.Y.: ‘Optimal reactive power dispatch using an adaptive genetic algorithm’, Int. J. Electr. Power Energy Syst., 1998, 20, (8), pp. 563569.
    16. 16)
      • 19. Timofte, R., De Smet, V., Van Gool, L.: ‘A+: adjusted anchored neighborhood regression for fast super-resolution’. Asian Conf. for Computer Vision (ACCV 2014), 2014, Springer International Publishing.
    17. 17)
      • 26. Wang, P., Hu, X., Xuan, B., et al: ‘Super resolution reconstruction via multiple frames joint learning’. 2011 Int. Conf. on Multimedia and Signal Processing, 2011, pp. 357361.
    18. 18)
      • 18. Timofte, R., Smet, V., Gool, L.: ‘Anchored neighborhood regression for fast example-based super-resolution’. Proc. of the IEEE Int. Conf. on Computer Vision, 2013, pp. 19201927.
    19. 19)
      • 6. Maalouf, A., Larabi, M.-C.: ‘Colour image super-resolution using geometric grouplets’, Image Process. IET, 2012, 6, (2), pp. 168180.
    20. 20)
      • 4. Tsai, R.Y., Huang, T.S.: ‘Multiframe image restoration and registration’, Adv. Comput. Vis. Image Process., 1984, 1, (2), pp. 317339.
    21. 21)
      • 1. Greenspan, H.: ‘Super-resolution in medical imaging’, Comput. J., 2008, 52, (1), pp. 4363.
    22. 22)
      • 21. 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.
    23. 23)
      • 2. Bidabadi, M., Natanzi, A.H.A., Mostafavi, S.A.: ‘Thermophoresis effect on volatile particle concentration in micro-organic dust flame’, Powder Technol., 2012, 217, pp. 6976.
    24. 24)
      • 29. Poli, R., Langdon, W.B.: ‘Schema theory for genetic programming with one-point crossover and point mutation’, Evol. Comput., 1998, 6, (3), pp. 231252.
    25. 25)
      • 32. Rudin, L.I., Osher, S., Fatemi, E.: ‘Nonlinear total variation based noise removal algorithms’, Phys. D, Nonlinear Phenom., 1992, 60, (1), pp. 259268.
    26. 26)
      • 20. Kato, T., Hino, H., Murata, N.: ‘Multi-frame image super resolution based on sparse coding’, Neural Netw., 2015, 66, pp. 6478.
    27. 27)
      • 13. Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: ‘Learning low-level vision’, Int. J. Comput. Vis., no date, 40, (1), pp. 2547.
    28. 28)
      • 15. Roweis, S.T., Saul, L.K.: ‘Nonlinear dimensionality reduction by locally linear embedding’, Science, 2000, 290, (5500), pp. 23232326.
    29. 29)
      • 5. Shayesteh Moghaddam, N.: ‘Toward patient specific long lasting metallic implants for mandibular segmental defects’ (University of Toledo, Ohio, USA, 2015).
    30. 30)
      • 14. Sun, J., Zheng, N.-N., Tao, H., et al: ‘Image hallucination with primal sketch priors’. 2003 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2003. Proc., IEEE Computer Society, 2000, pp. II-72936.
    31. 31)
      • 33. Wang, Z., Bovik, A.C., Sheikh, H.R., et al: ‘Image quality assessment: from error visibility to structural similarity’, IEEE Trans. Image Process., 2004, 13, (4), pp. 600612.
    32. 32)
      • 34. Dai, D., Timofte, R., Van Gool, L.: ‘Jointly optimized regressors for image super-resolution’. Computer Graphics Forum, 2015, pp. 95104.
    33. 33)
      • 28. Murray, J.F., Kreutz-Delgado, K.: ‘Learning sparse overcomplete codes for images’, J. VLSI Signal Process. Syst. Signal Image. Video Technol., 2006, 46, (1), pp. 113.
    34. 34)
      • 24. Walha, R., Drira, F., Lebourgeois, F., et al: ‘Sparse coding with a coupled dictionary learning approach for textual image super-resolution’. 2014 22nd Int. Conf. on Pattern Recognition (ICPR), 2014, pp. 44594464.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2016.0273
Loading

Related content

content/journals/10.1049/iet-ipr.2016.0273
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading