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Composite high frequency predictive scheme for efficient 2-D up-scaling performance

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Abstract

This paper presents a new composite scheme to tackle the non-uniform blurring that arises because of image up-scaling. The image up-scaling results in undesirable blurring artifacts at the edge and fast changing regions due to high frequency (HF) and very high frequency (VHF) degradation. Most of the widely used interpolation schemes fail to predict the HF information in up-scaled image which consequently results in undesirable blurring. In order to overcome this problem, the proposed composite scheme is developed by combining a pre-processing and a post-processing operation to efficiently restore HF and VHF information in the up-scaled image. The pre-processing operation makes use of an iterative global sharpening scheme prior to image up-scaling to boost up the high frequency information so as to alleviate the extent of blurring in the up-scaled images. The post-processing scheme is operated on the up-scaled images to enhance and predict the HF information using a local statistics based local adaptive Laplacian filter. The appropriate fusion of pre-processing and post-processing operations results in more accurate prediction of HF in the up-scaled images. Experimental results reveal that the proposed composite scheme gives much less blurring in comparison to the standalone schemes and outperform most of the widely used interpolation schemes in terms of objective and subjective measures.

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References

  1. Acharya A, Meher S (2013a) Region adaptive unsharp masking based DCT interpolation for efficient video intra-frame up-sampling. IJCA Special Issue on Electric design and Signal Processing ICEDSP(3): 29–33, Feb 2013

  2. Acharya A, Meher S (2013b) No reference fuzzy unsharp masking based DCT interpolation for better 2D up-upsampling. In Proc, IEEE International Conference on Fuzzy systems, Hyderabad, Jul 2013

  3. Acharya A, Meher S (2013c) Region based Laplacian post-processing for better 2-D up- sampling. in Proc. of annual IEEE International Conference INDICON, IIT Bombay, Dec 2013

  4. Acharya A, Meher S (2015) An Improved image super-resolution using local adaptive unsharp masking. In Proc IEEE International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), Vishakapatnam, Jan 2015

  5. Aly HA, Dubois E (2005) Image up-sampling using total-variation regularization with a new observation model. IEEE Trans Image Process 14(l0):1647–1659

    Article  Google Scholar 

  6. Blu T, Thevenaz P, Unser M (2004) Linear interpolation revitalized. IEEE Trans Image Process 13(5):710–719

    Article  MathSciNet  Google Scholar 

  7. Burger W, Burge MJ (2009) Principles of digital image processing: core algorithms. Springer, pp 231–232

  8. Chena MJ, Huang CH, Lee WL (2005) A fast edge-oriented algorithm for image interpolation. Image Vis Comput 23:791–798

    Article  Google Scholar 

  9. Cho M-K, Lee B-U (2006) Discrete cosine transform domain image resizing using correlation of discrete cosine transform coefficients. J Electron Imag 15(3):033009

    Article  Google Scholar 

  10. Dugad R, Ahuja N (2001) A fast scheme for image size change in the compressed domain. IEEE Trans Circuit, Syst, Video Technology 11:461–474

    Article  Google Scholar 

  11. Hou HS, Andrews HC (1978) Cubic splines for image interpolation and digital filtering. IEEE Trans. on Acoust., Speech, Signal Process ASSP-26(6):508–517

    MATH  Google Scholar 

  12. Hung K-W, Siu W-C (2010) Improved image interpolation using bilateral filter for weighted least square estimation. Proc IEEE Int Conf Image Processing (ICIP 2010), pp 3297–3300, 26–29 September, 2010, Hong Kong

  13. Hung K-W, Siu W-C (2012) Robust soft-decision interpolation using weighted least squares. IEEE Trans Image Process 21(3):1061–1069

    Article  MathSciNet  MATH  Google Scholar 

  14. Hung K-W, Siu W-C (2013) Hybrid DCT-Wiener-based interpolation via learnt Wiener filter. In Proc IEEE Int Conf Acoust, Speech, Signal Process, pp 1419–1423, May 2013

  15. Keys RG (1981) Cubic convolution interpolation for digital image processing. IEEE Trans on Acoust, Speech, Signal Process ASSP-29(6):1153–1160

    Article  MathSciNet  MATH  Google Scholar 

  16. Lee SW, Paik JK (1993) Image interpolation using adaptive fast Bspline filtering. Proc IEEE Int Conf Acoustics, Speech, Signal Processing (ICASSP 1993) 5:177–180

    Google Scholar 

  17. Lehmann T, Gonner C, Spitzer K (2001) Addendum: B-spline interpolation in medical image processing. IEEE Trans on Med Imag 20(7):660–665

    Article  Google Scholar 

  18. Li M, Nguyen (2008) Markov random field model-based edge directed image interpolation. IEEE Trans Image Process l7(7):1121–1128

    MathSciNet  Google Scholar 

  19. Lim H, Park HW (2011) A ringing-artifact reduction method for block DCT-based image resizing. IEEE Trans Circuits Syst Video Technol 21(7):879–889

    Article  Google Scholar 

  20. Mukherjee J, Mitra SK (2002) Image resizing in the compressed domain using subband DCT. IEEE Trans Circuits, Syst, Video Technology 12:620–627

    Article  Google Scholar 

  21. Ren J, Liu J, Bai W, Guo Z (2011) Similarity modulated block estimation for image interpolation. Proc IEEE Int Conf Image Processing (ICIP 2011), pp 1 l77–ll80, 11–14 Sept 2011, Brussels, Belgium

  22. Sajjad M, Ejaz N, Mehmood I, Baik SW (2013) Digital image super-resolution using adaptive interpolation based Gaussian function. Multi Tool Appl, Springer 74(20):8961–8977

    Article  Google Scholar 

  23. Smit T, Smith MR, Nichols ST (1990) Efficient sinc function interpolation technique for center padded data. IEEE Trans Acoust, Speech, Signal Processing 38:1512–1517

    Article  Google Scholar 

  24. Tang K, Au OC, Fang L, Yu Z, Guo Y (2011) Image interpolation using autoregressive model and gauss-seidel optimization. Proc Sixth Int Conf on Image and Graphics (ICIG 2011), pp 66–69, 12–15 Aug 2011, Hefei, Anhui, China

  25. Wong C-S, Siu W-C (2010a) Further improved edge-directed interpolation and fast EDI for SDTV to HDTV Conversion. Proc 18th European Signal Processing Conference (EUSIPCO 2010), pp 309–313, 23–27 Aug 2010, Aalborg Denmark

  26. Wong C-S, Siu W-C (2010b) Adaptive directional window selection for edge- directed interpolation. Proc ICCCN 2010, Workshop on Multimedia Computing and Communications, MMCI, No. 4, pp 1–6, 2–5 Aug 2010, Zurich, Switzerland

  27. Wu Z, Yu H, Chen CW (2010) A new hybrid DCT wiener based interpolation scheme for video intraframe up-sampling. IEEE signal processing letters 17(10):827–830

    Article  Google Scholar 

  28. Yang S, Kim Y, Jeong J (2008) Fine edge-preserving technique for display devices. IEEE Trans on Consumer Electronics 54(4):1761–1769

    Article  Google Scholar 

  29. Yaroslavsky LP (1996) Signal sinc-interpolation: a fast computer algorithm. Bioimaging 4:225–231

    Article  Google Scholar 

  30. Ye W, Entezari A (2011) A geometric construction of multivariate sinc functions. IEEE Transaction on Image processing 19(12):2969

    MathSciNet  MATH  Google Scholar 

  31. Zhang X, Wu X (2008) Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation. IEEE Trans Image Process 17(6):887–896

    Article  MathSciNet  Google Scholar 

Download references

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Acharya, A., Meher, S. Composite high frequency predictive scheme for efficient 2-D up-scaling performance. Multimed Tools Appl 77, 2153–2189 (2018). https://doi.org/10.1007/s11042-017-4346-1

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  • DOI: https://doi.org/10.1007/s11042-017-4346-1

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