Abstract
This paper proposed an improved method for contrast enhancement of real world hyperspectral images. The proposed method consists of two stages: In first stage the poor quality of image is processed by adaptive histogram equalization in spatial domain and in second stage the output of first stage is further processed by adaptive filtering for image enhancement in frequency domain. Simulation and experimental results on benchmark real world hyperspectral image database demonstrates that proposed method provides better results as compared to other state-of-art contrast enhancement techniques such as alpha rooting (AR), multi contrast enhancement (MCE), multi-contrast enhancement with dynamic range compression (MCEDRC), brightness preserving dynamic fuzzy histogram equalization (BPDFHE). Proposed method performs better for different dark and bright real world hyperspectral images by adjusting their contrast very frequently. Proposed method is simple and efficient approach for contrast enhancement of real world hyperspectral images. This method can be used in different applications where images are suffering from various contrast problems.
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References
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley Publishing Company (1992)
Stark, J.A.: Adaptive Contrast Enhancement Using Generalization of Histogram Equalization. IEEE Transactions on Image Processing 9(5), 889–906 (2000)
Caselles, V., Lisani, J.L., Morel, J.M., Sapiro, G.: Shape Preserving Local Histogram Modification. IEEE Transactions on Image Processing 8(2), 220–230 (1998)
Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Romeny, B.T.H., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Computer Vision, Graphics and Image Processing 39(3), 355–368 (1987)
ZuiderveldK: Graphics Gems IV. In: Contrast Limited Adaptive Histogram Equalization, vol. 5, ch. VIII, pp. 474–485. Academic Press, Cambridge (1994)
Aghagolzadeh, S., Ersoy, O.K.: Transform Image Enhancement. Optical Engineering 31, 614–626 (1992)
Chen, S.D., Ramli, A.R.: Preserving Brightness in Histogram Equalization Based Contrast Enhancement Techniques. Digital Signal Processing 14(5), 413–428 (2004)
Chen, S.D., Ramli, A.R.: Contrast Enhancement Using Recursive Mean-Separate Histogram Equalization For Scalable Brightness Preservation. IEEE Transactions on Consumer Electronics 49(4), 1301–1309 (2003)
Coltuc, D., Bolon, P., Chassery, J.M.: Exact Histogram Specification. IEEE Transactions on Image Processing 15(5), 1143–1151 (2006)
Kim, Y.T.: Contrast Enhancement Using Brightness Preserving Bi-histogram Equalization. IEEE Transactions on Consumer Electronics 43(1), 1–8 (1997)
Lee, S.: An Efficient Content-Based Image Enhancement In The Compressed Domain Using Retinex Theory. IEEE Transactions on Circuits Systems and Video Technology 17(2), 199–213 (2007)
Sheet, D., Garud, H., Suveer, A., Mahadevappa, A.M., Chatterjee, J.: Brightness Preserving Dynamic Fuzzy Histogram Equalization. IEEE Transactions on Consumer Electronics 56(4), 2475–2480 (2010)
Tang, J., Peli, E., Acton, S.: Image Enhancement Using A Contrast Measure In the Compressed Domain. IEEE Signal Processing Letter 10(10), 289–292 (2003)
Wang, Y., Chen, Q., Zhang, B.: Image Enhancement Based on Equal Area Dualistic Sub-Image Histogram Equalization Method. IEEE Transactions on Consumer Electronics 45(1), 68–75 (1999)
Celik, T., Tjahjadi, T.: Contextual and Variational Contrast Enhancement. IEEE Transactions on Image Processing 20(12), 3431–3441 (2011)
Hassan, N., Akamatsu, N.: A New Approach For Contrast Enhancement Using Sigmoid Function. The International Arab Journal of Information Technology 1(2), 221–225 (2004)
Agaian, S., Silver, B., Panetta, K.: Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy. IEEE Transactions on Image Processing 16(3), 741–757 (2007)
Chen, S.D., Ramli, A.R.: Minimum Mean Brightness Error Bi-Histogram Equalization in Contrast Enhancement. IEEE Transactions on Image Processing 49(4), 1310–1319 (2003)
Lal, S., Chandra, M., Upadhyay, G.K.: Contrast Enhancement of Compressed Image in Wavelet Based Domain. In: The Proceedings of International Conference on Signal Recent Advancements in Electrical Sciences, ICRAES 2010, Tiruchengonde (TN) India, January 08-09, pp. 479–489 (2010)
Chakrabarti, A., Zickler, T.: Statistics of Real-World Hyperspectral Images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 193–200 (2011)
Panetta, K., Zhou, Y., Agaian, S., Jia, H.: Nonlinear Unsharp Masking for Mammogram Enhancement. IEEE Transactions on Information Technology in Biomedicine 15(6), 918–928 (2011)
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© 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Lal, S., Kumar, R., Chandra, M. (2013). An Improved Method for Contrast Enhancement of Real World Hyperspectral Images. In: Singh, K., Awasthi, A.K. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Networks. QShine 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37949-9_23
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DOI: https://doi.org/10.1007/978-3-642-37949-9_23
Publisher Name: Springer, Berlin, Heidelberg
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