http://iet.metastore.ingenta.com
1887

Noise-insensitive and edge-preserving resolution upconversion scheme for digital image based on the spatial general autoregressive model

Noise-insensitive and edge-preserving resolution upconversion scheme for digital image based on the spatial general autoregressive model

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study proposes an edge-preserving image interpolation algorithm for both clean images and polluted images. First, the structure of an image window is learnt adaptively by using a spatial general autoregressive (SGAR) model that is a uniform expression for both linear and non-linear autoregressive (AR) models. Parameters of the SGAR model are estimated in a moving window in the input low-resolution image by using the robust generalised M-estimator. Next, the interpolation model is established from the learnt model and a new feedback mechanism in accordance with the residual sum of squares minimisation principle. Finally, the gradient simulated annealing algorithm is used to solve the interpolation model, which can rapidly converge to the global optimum in probability with the help of gradient information. Experiments have been performed using worldwide datasets to evaluate the performance of the authors method. The results demonstrate that their method is superior to a recent AR model-based method and is bicubic, especially when images are polluted by noise such as Gaussian noise, Poisson noise, impulse noise, or a combination of these.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
      • 9. Battiato, S., Rundo, F., Stanco, F.: ‘ALZ: adaptive learning for zooming digital images’. 25th IEEE Int. Conf. on Consumer Electronics, Las Vegas, NV, 10–14 January 2007, pp. 209210.
    10. 10)
      • 10. Battiato, S., Giuffrida, E.U., Rundo, F.: ‘A cellular neural network for zooming digital colour images’. 26th IEEE Int. Conf. on Consumer Electronics, Las Vegas, NV, 9–13 January 2008, pp. 153154.
    11. 11)
    12. 12)
    13. 13)
      • 13. Jakhetiya, V., Kumar, A., Tiwari, A.K.: ‘Image interpolation by adaptive 2-D autoregressive modeling’. Second Int. Conf. on Digital Image Processing, Singapore, Singapore, 26 February 2010, pp. 16.
    14. 14)
    15. 15)
      • 15. Gao, X.W., Zhang, J., Jiang, F., et al: ‘High-quality image interpolation via local autoregressive and nonlocal 3-D sparse regularization’. 2012 IEEE Visual Communications and Image Processing, New York, 27–30 November 2012, pp. 16.
    16. 16)
    17. 17)
      • 17. Zhang, X.F., Ma, S.W., Zhang, Y.B., et al: ‘Nonlocal edge-directed interpolation’. Advances in Multimedia Information Processing, Bangkok, Thailand, 15–18 December 2009, pp. 11971207.
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
      • 27. Zhan, Y., Li, S.J., Li, M.: ‘Local and nonlocal regularization to image interpolation’, Math. Probl. Eng., 2014, 2014, pp. 18.
    28. 28)
    29. 29)
    30. 30)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2015.0095
Loading

Related content

content/journals/10.1049/iet-ipr.2015.0095
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address