Comparative Study of Three Image Enhancement Techniques for Geospatial Data
American Journal of Mathematical and Computer Modelling
Volume 4, Issue 2, June 2019, Pages: 45-51
Received: Apr. 28, 2019; Accepted: Jun. 4, 2019; Published: Jul. 2, 2019
Views 656      Downloads 138
Peter Ekow Baffoe, Department of Geomatic Engineering, Faculty of Mineral Resources Technology, University of Mines and Technology, Tarkwa, Ghana
Article Tools
Follow on us
Processing images for Geomatic works is one of the most difficult techniques. The image enhancement algorithms have direct effect on the quality of images. It is normally done to improve visual appearance and provide a better technique for future automated image processing. Sources of mages include satellite, photography and aerial photogrammetry that are used for geospatial data processing. These images suffer from poor contrast and noise. To use these images effectively, there is the need to enhance the contrast and remove the noise from the image to increase its quality. There are different techniques for image enhancement but this study focused on image interpolation. This multi-resolution technique is useful for variety of fields where fine and minor details are important. In this research, the Nearest Neighbor, Bilinear and Bicubic image interpolation algorithm were compared. Using the aforementioned techniques, two images were enhanced in order to compare their strengths and processing speed. The results of the algorithm of Nearest Neighbor had low computational time, low complexity of algorithm and poor image quality. On the other hand, the algorithms of Bilinear and Bicubic had average and high computational time, average and high complexity of algorithm and average and good image quality respectively.
Image Enhancement, Interpolation Algorithm, Geospatial
To cite this article
Peter Ekow Baffoe, Comparative Study of Three Image Enhancement Techniques for Geospatial Data, American Journal of Mathematical and Computer Modelling. Vol. 4, No. 2, 2019, pp. 45-51. doi: 10.11648/j.ajmcm.20190402.13
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Robert G. (2012), “Cubic Convolution Interpolation for Digital Image Processing”, IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-29, Issue 6, pp. 1153-1160.
Thilina S. (2014), “Digital Image Zooming”, Accessed: March 04, 2018.
Maini, R. and Aggarwal, H. (2010), “A Comprehensive Review of Image Enhancement Techniques”, Journal of Computing, Vol. 2, Issue 3, pp. 8-13.
Huijian, H., Changjin, L. and Jiefei, F. (2012), “The Non-Uniform B-Spline Interpolation Image Enlargement Algorithm”, Journal of Computational and Theoretical Nanoscience, 7 (1), 277-280.
R. G. Keys, Cubic convolution interpolation for digital image processing, IEEE Transactions on Acoustics, Speech, Signal Processing 29 (6) (1981) 1153–1160.
S. D. Bayraker, R. M. Mersereau, A new method for directional image interpolation, IEEE International Conference on Acoustics, Speech, and Signal Processing 4 (1995) 2383–2386.
L. Khriji, M. Gabbouj, Directional-vector rational filters for color image interpolation Proceedings of the Tenth International Conference on Microelectronics (1998), pp. 236–240.
Jiefei, F. and Han Huijian. H. (2010),”Image enlargement based on non-uniform B-spline interpolation algorithm”. Journal of Computer Applications, Vol. 30, Issue 1, pp. 82-84.
Bedi, S. and Khandelwal, R. (2013), “Various Image Enhancement Techniques - A Critical Review”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, pp. 55-67.
Carlson, B. (2012), “Image Interpolation and Filtering”, IEEE Trans on ASSP, Vol. 6, pp. 32-45.
Kassab A. (2012), “Image Enhancement Methods and Implementation in Matlab”, MSc Project, Zapadoceska Univerzita V Plzni, 55pp.
C. Lee, B. Zeng, A novel interpolation scheme for rectangularly subsampled images, International Conference on Image Processing 3 (1999) 787–791.
B. Zeng, M. S. Fu, C. C. Chuang, New interleaved hierarchical interpolation with median-based interpolators for progressive image transmission, Signal Processing 81 (2001) 431–438.
H. Jiang, C. Moloney, A new direction adaptive scheme for image interpolation, International Conference on Image Processing 3 (2002) 369–372.
A. M. Darwish, M. S. Bedair, S. I. Shaheen, Adaptive resampling algorithm for image zooming, IEE Proceedings-Vision Image and Signal Processing 144 (4) (1997) 207–212.
Maeland E. and Gupta S. (2012), “On the Comparison of Interpolation Methods”, IEEE Transactions on Medical Imaging, Vol. 7, No. 6, pp. 213-217.
Russell, W., S. (1995), “Polynomial interpolation schemes for internal derivative distributions on structured grids,” Applied Numerical Mathematics, Vol. 17, No. 2, pp. 129–171.
Hou, H. and Andrews, H. (1978), “Cubic splines for image interpolation and digital filtering,” IEEE Trans. Acoust. Speech Signal Process, Vol. 26, No. 6, pp. 508–517.
Han, D. (2013), “Comparison of Commonly Used Image Interpolation Methods”, Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013), pp. 1-4.
Gao, S. and Gruev, V. (2011), “Bilinear and Bicubic Interpolation Methods for Division of Focal Plane Polarimeters”, Open Science Journal, Vol. 19, Issue 27, pp. 1-13.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186