Implementation and Comparison of Image Enhancement Techniques Using Low Resolution IRS-1C LISS III Image
DOI:
https://doi.org/10.51983/ajcst-2018.7.1.1828Keywords:
Image enhancement, IRS-1C LISS III Image, Partial Differential Equations, Anisotropic Diffusion and Histogram EqualizationAbstract
Image enhancement is the primary step in image processing. Image enhancement improves the interpretation and makes the image visually clear. In this process pixels of input image were fine-tuned, so that the results are more suitable for display or further image analysis. Numerical manipulation of digital image includes pre-processing as the preliminary step of analysis. Contrast manipulation, spatial filtering, noise suppression and color processing are different methods of image enhancement. Choosing suitable method for satellite image enhancement depends on the application. This paper aims to compare results of various image enhancement techniques using an IRS-1C LISS III satellite image. It attempts to assess enhancement techniques. Shortcomings and general requirements in enhancement techniques were also discussed. This study gives promising directions on research using IRS-1C LISS III image enhancement for future research.
References
S. Bedi and R. Khandelwal, "Various Image Enhancement Techniques- A Critical Review," International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 3, 2013.
D. C. Wang, A. H. Vagnucci, and C. Li, "Digital Image Enhancement: a Survey," Computer Vision, Graphics, and Image Processing, vol. 24, no. 3, pp. 363–381, 1983.
R. Hummel, "Image Enhancement by Histogram Transformation," Computer Graphics and Image Processing, vol. 6, no. 2, pp. 184–195, 1977.
R. A. Schowengerdt, "Techniques For Image Processing And Classifications In Remote Sensing," Academic Press, 2012.
M. V. Sarode and P. R. Deshmukh, "Reduction of Speckle Noise and Image Enhancement of Images using Filtering Technique," International Journal of Advancements in Technology, vol. 11, 2011.
R. Maini and H. Aggarwal, "Study and Comparison of Various Image Edge Detection Techniques," International Journal of Image Processing, vol. 3, no. 1, pp. 1–11, 2009.
S. S. Alamri, N. Kalyankar, and S. Khamitkar, "Linear and Non-Linear Contrast Enhancement Image," International Journal of Computer Science and Network Security, vol. 10, no. 2, pp. 139–143, 2010.
K. Tang, J. Astola, and Y. Neuvo, "Nonlinear Multivariate Image Filtering Techniques," IEEE Transactions on Image Processing, vol. 4, no. 6, pp. 788–798, 1995.
C. Kenney, Y. Deng, B. Manjunath, and G. Hewer, "Peer Group Image Enhancement," IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 326–334, 2001.
D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, "Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise," IEEE Transactions on Pattern Analysis and Machine Intelligence, no. 2, pp. 165–177, 1985.
R. Fattal, "Single Image Dehazing," ACM transactions on graphics (TOG), vol. 27, no. 3, p. 72, 2008.
W. Frei, "Image Enhancement by Histogram Hyperbolization," Computer Graphics and Image Processing, vol. 6, no. 3, pp. 286–294, 1977.
R. Alley, "Algorithm Theoretical Basis Document for: Decorrelation Stretch," 1999.
M. V. Dasu, V. Anitha, F. Shaik, and B. A. Rahim, "Feature Extraction of Satellite Images using Decorrelation Stretching and Color Scatter Plots," Digital Image Processing, vol. 3, no. 14, pp. 873–877, 2011.
S. M. Chao and D. M. Tsai, "An Improved Anisotropic Diffusion Model For Detail And Edge-Preserving Smoothing," Pattern Recognition Letters, vol. 31, no. 13, pp. 2012–2023, 2010.
M. H. Xie and Z. M. Wang, "Edge-directed Enhancing Based Anisotropic Diffusion Denoising," Dianzi Xuebao (Acta Electronica Sinica), vol. 34, no. 1, pp. 59–64, 2006.
P. Liu, F. Huang, G. Li, and Z. Liu, "Remote-Sensing Image Denoising using Partial Differential Equations and Auxiliary Images as Priors," IEEE Geoscience and Remote Sensing Letters, vol. 9, no. 3, pp. 358–362, 2012.
B. Chen, J. L. Cai, W. S. Chen, and Y. Li, "A Multiplicative Noise Removal Approach Based on Partial Differential Equation Model," Mathematical Problems in Engineering, 2012.
J. A. Stark, "Adaptive Image Contrast Enhancement using Generalizations of Histogram Equalization," IEEE Transactions On Image Processing, vol. 9, no. 5, pp. 889–896, 2000.
J. Singhai and P. Rawat, "Image Enhancement Method for Underwater, Ground and Satellite Images Using Brightness Preserving Histogram Equalization with Maximum Entropy," in Conference on Computational Intelligence and Multimedia Applications, 2007, International Conference on, vol. 3, pp. 507–512, IEEE, 2007.
D. Menotti, A. d. A. Araujo, G. L. Pappa, L. Najman, and J. Facon, "Contrast Enhancement in Digital Imaging using Histogram Equalization," in VII Workshop of Theses and Dissertations on Computer Graphics and Image Processing (WTDCGPI), part of SIBGRAPI, pp. 10, 2008.
I. Pitas and A. Venetsanopoulos, "Nonlinear Mean Filters in Image Processing," IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 34, no. 3, pp. 573–584, 1986.
K. He, J. Sun, and X. Tang, "Single image haze removal using dark channel prior," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2341–2353, 2011.
R. H. Chan, C. W. Ho, and M. Nikolova, "Salt-and-pepper Noise Removal by Median Type Noise Detectors and Detail-Preserving Regularization," IEEE Transactions on Image Processing, vol. 14, no. 10, pp. 1479–1485, 2005.
I. Jolliffe, Principal component analysis, Wiley Online Library, 2002.
H. Abdi and L. J. Williams, "Principal Component Analysis," Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 4, pp. 433–459, 2010.
J. Weickert, "Anisotropic Diffusion in Image Processing," vol. 1. Teubner Stuttgart, 1998.
A. C. Rencher, "Principal Component Analysis Methods of Multivariate Analysis," 2nd Ed., pp. 380–407, 2002.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 The Research Publication
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
