Image Magnification Using Adaptive Interpolationby Pixel Level Data-Dependent Geometrical Shapes

Authors: Muhammad Sajjad, Naveed Khattak, Noman Jafri

Abstract:

World has entered in 21st century. The technology of computer graphics and digital cameras is prevalent. High resolution display and printer are available. Therefore high resolution images are needed in order to produce high quality display images and high quality prints. However, since high resolution images are not usually provided, there is a need to magnify the original images. One common difficulty in the previous magnification techniques is that of preserving details, i.e. edges and at the same time smoothing the data for not introducing the spurious artefacts. A definitive solution to this is still an open issue. In this paper an image magnification using adaptive interpolation by pixel level data-dependent geometrical shapes is proposed that tries to take into account information about the edges (sharp luminance variations) and smoothness of the image. It calculate threshold, classify interpolation region in the form of geometrical shapes and then assign suitable values inside interpolation region to the undefined pixels while preserving the sharp luminance variations and smoothness at the same time. The results of proposed technique has been compared qualitatively and quantitatively with five other techniques. In which the qualitative results show that the proposed method beats completely the Nearest Neighbouring (NN), bilinear(BL) and bicubic(BC) interpolation. The quantitative results are competitive and consistent with NN, BL, BC and others.

Keywords: Adaptive, digital image processing, imagemagnification, interpolation, geometrical shapes, qualitative &quantitative analysis.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1060631

References:

[1] Adobe Photoshop. Adobe Systems.
[2] J. Allebach and P. W. Wong. "Edge-directed interpolation". In Proceedings of IEEE International Conference on Image Processing Vol. 3, pages 707- 710, 1996.
[3] W. T. Freeman, T. R. Jones, and E. C. Pasztor. Example based Superresolution. IEEE Computer Graphics and Applications, 22(2):56-65, 2002.
[4] ÔÇÿDigital Photography Review- http://www.dpreview.com/
[5] Henry Johan, Tomoyuki Nishita, "A Progressive Refinement Approach for Image Magnification", Proceedings of the 12th pacific conference on Computer Graphics and Applications (PG-04)1550-4085/04 IEEE Computer Society.
[6] Muneeb,Naveed khattak "An Edge Preserving Locally Adaptive Anti- Aliasing Zooming Algorithm with Diffused Interpolation" IEEE Computer Society The 3rd Canadian Conference on computer and Robotic Vision (CRV-06) Volume 00,page 49,Miami Fl 33189,23-24 Mar 2006 IEEE.
[7] A. Hertzmann, C. E. Jacobs, N. Oliver, B. Curless, and D. H. Salesin. Image analogies. In Proceedings of SIGGRAPH 2001, pages 327-340, 2001.
[8] H. S. Hou and H. C. Andrews. Cubic splines for image interpolation and digital filtering. IEEE Transactions on Acoustics, Speech, Signal Processing, 26(6):508-517, 1978.
[9] K. Jensen and D. Anastassiou. Subpixel edge localization and the interpolation of still images. IEEE Transactions on Image Processing, 4(3):285-295, 1995.
[10] R. Keys. Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech, Signal Processing, 29(6):1153-1160, 1981.
[11] S. W. Lee and J. K. Paik. Image interpolation using adaptive fast Bspline filtering. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing Vol. 5, pages 177-179, 1993.
[12] X. Li and M. T. Orchard. New edge-directed interpolation. IEEE Transactions on Image Processing, 10(10):1521-1527, 2001.
[13] F. Malgouyres and F. Guichard. Edge direction preserving image zooming: a mathematical and numerical analysis. SIAM Journal on Numerical Analysis, 39(1):1-37, 2001.
[14] B. S. Morse and D. Schwartzwald. Image magnification using level-set reconstruction. In Proceedings of IEEE International Conference on Computer Vision, pages 333-341, 2001.
[15] D. D. Muresan and T. W. Parks. New image interpolation techniques. In Proceedings of IEEE 2000 Western New York Image Processing Workshop, 2000.
[16] D. D. Muresan and T. W. Parks. Adaptive, optimal-recovery image interpolation. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing Vol. 3, pages 1949-1952, 2001.
[17] D. D. Muresan and T. W. Parks. Optimal recovery approach to image interpolation. In Proceedings of IEEE International Conference on Image Processing Vol. 3, pages 848-851, 2001.
[18] D. D. Muresan and T.W. Parks. Adaptively quadratic (AQua) image interpolation. IEEE Transactions on Image Processing, 13(5):690-698, 2004.
[19] X. Yu, B. S. Morse, and T. W. Sederberg. Image reconstruction using data-dependent triangulation. IEEE Computer Graphics and Applications, 21(3):62-68, 2001.
[20] S. Battiato, G. Gallo, and F. Stanco, "A New Edge-Adaptive Zooming Algorithm for Digital Images", in Proc. Signal Processing and Communication SPC 2000, pp. 144ÔÇö149, Spain, 2000.
[21] R. C. Gonzales and R. E. Woods digital image processing (Pearson Prentice Hall, Eleventh Indian Reprint, 2005).