Enhancement of Hyperspectral Real World Images Using Hybrid Domain Approach

Enhancement of Hyperspectral Real World Images Using Hybrid Domain Approach

Автор: Shyam Lal, Rahul Kumar

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 5 vol.5, 2013 года.

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This paper presents enhancement of hyperspectral real world images using hybrid domain approach. The proposed method consists of three phases: In first phase the discrete wavelet transform is applied and approximation coefficient is selected. In second phase approximation coefficient of discrete wavelet transform of image is process by automatic contrast adjustment technique and in third phase it takes logarithmic of output of second phase and after that adaptive filtering is applied for image enhancement in frequency domain. To judge the superiority of proposed method the image quality parameters such as measure of enhancement (EME) and measure of enhancement factor (EMF) is evaluated. Therefore, a better value of EME and EMF implies that the visual quality of the enhanced image is good. Simulation results indicates that proposed method provides better results as compared to other state-of-art contrast enhancement algorithms for hyperspectral real world images. The proposed method is efficient and very effective method for contrast enhancement of hyperspectral real world images. This method can also be used in different applications where images are suffering from different contrast problems.

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Contrast enhancement, Hyperspectral real world image, Image processing, Adaptive filtering

Короткий адрес: https://sciup.org/15012706

IDR: 15012706

Список литературы Enhancement of Hyperspectral Real World Images Using Hybrid Domain Approach

  • R.C. Gonzalez and R.E. Woods. Digital Image Processing. Pearson Prentice Hall, Third edition, 2008.
  • P. Robinson, Y. Roodt and A. Nel. Adaptive Multi-Scale Retinex algorithm for contrast enhancement of real world scenes. In the Proceedings of Twenty-Third Annual Symposium of the Pattern Recognition Association of South Africa, Pretoria, South Africa, Edited by Alta de Waal, November 29-30, 2012.
  • P. E. Robinson and W. A. Clarke. Sharpening and contrast enhancement of atmospheric turbulence degraded video sequences. In the Proceedings of the Twenty-First Annual Symposium of the Pattern Recognition Association of Pretoria, South Africa, 2010.
  • J. Zimmerman, S. Pizer, E. Staab, J. Perry W. McCartney and B. Brenton. An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement. IEEE Transactions on Medical Imaging, 1988, 7: 304–312.
  • A. Bovik. Handbook of Image and Video Processing. Academic Press, 2000.
  • J.A Stark. Adaptive Contrast Enhancement Using Generalization of Histogram Equalization. IEEE Transactions on Image Processing, 2000, 9(5): 889-906.
  • V. Caselles, J.L. Lisani, J.M. Morel, and G. Sapiro. Shape Preserving Local Histogram Modification. IEEE Transactions on Image Processing, 1998, 8(2): 220-230.
  • S.M. Pizer, E.P. Amburn, J.D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B.T.H. Romeny, J.B. Zimmerman, and K. Zuiderveld. Adaptive histogram equalization and its variations. Computer Vision, Graphics and Image Processing, 1987, 39(3): 355-368.
  • K. Zuiderveld. Contrast Limited Adaptive Histogram Equalization. Chapter VIII.5, Graphics Gems IV, Cambridge, MA, Academic Press, 1994, 474-485.
  • S.D. Chen and A.R. Ramli. Preserving Brightness in Histogram Equalization Based Contrast Enhancement Techniques. Digital Signal Processing, 2004, 14(5): 413-428.
  • S.D. Chen and A.R. Ramli. Contrast Enhancement Using Recursive Mean-Separate Histogram Equalization for Scalable Brightness Preservation. IEEE Transactions on Consumer Electronics, 2003, 49(4): 1301-1309.
  • D. Coltuc, P. Bolon and J.M. Chassery. Exact Histogram Specification. IEEE Transactions on Image Processing, 2006, 15(5): 1143-1151.
  • Y.T. Kim. Contrast Enhancement Using Brightness Preserving Bi-histogram Equalization. IEEE Transactions on Consumer Electronics, 1997, 43(1): 1-8.
  • S. Lee. An Efficient Content-Based Image Enhancement in the Compressed Domain Using Retinex Theory. IEEE Transactions on Circuits Systems and Video Technology, 2007, 17(2): 199-213.
  • D. Sheet, H. Garud, A. Suveer, A.M. Mahadevappa and J. Chatterjee. Brightness Preserving Dynamic Fuzzy Histogram Equalization. IEEE Transactions on Consumer Electronics, 2010, 56(4): 2475-2480.
  • S.D. Chen and A.R. Ramli. Minimum Mean Brightness Error Bi-Histogram Equalization in Contrast Enhancement. IEEE Transactions on Image Processing, 2003, 49(4):1310-1319.
  • Y. Wang, Q. Chen and B. Zhang. Image Enhancement Based on Equal Area Dualistic Sub-Image Histogram Equalization Method. IEEE Transactions on Consumer Electronics, 1999, 45(1): 68-75.
  • A. M. Reza. Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology, 2004, 38: 35–44.
  • D. Menotti, L. Najman, J. Facon and A. de Araujo. Multi-histogram equalization methods for contrast enhancement and brightness preserving. IEEE Transactions on Consumer Electronics, 2007, 53: 1186–1194.
  • S. Lal, M. Chandra, G.K. Upadhyay. 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, 2010, 479-489.
  • S. Aghagolzadeh and O.K. Ersoy. Transform Image Enhancement. Optical Engineering, 1992, 31: 614-626.
  • J. Tang, E. Peli and S. Acton. Image Enhancement Using a Contrast Measure in the Compressed Domain. IEEE Signal Processing Letter, 2003, 10 (10): 289-292.
  • T. Celik and T. Tjahjadi. Contextual and Variational Contrast Enhancement. IEEE Transactions on Image Processing, 2011, 20(12): 3431-3441.
  • N., Hassan and N. Akamatsu. A New Approach for Contrast Enhancement Using Sigmoid Function. The International Arab Journal of Information Technology, 2004, 1(2): 221-225.
  • S. Agaian, B. Silver and K. Panetta. Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy. IEEE Transactions on Image Processing, 2007, 16(3):741-757.
  • S. Lal, R. Kumar and M. Chandra. An Improved Method for Contrast Enhancement of Real World Hyperspectral Images. In the Proceedings of 9th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (Qshine-2013), jointly organized by Gautam Buddha University, Gr. Noida, India and EAI, USA, January 11-12, 2013.
  • A. Chakrabarti and T. Zickler. Statistics of Real-World Hyperspectral Images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, 193-200.
  • K. Panetta,Y. Zhou, S. Agaian and H. Jia. Nonlinear Unsharp Masking for Mammogram Enhancement. IEEE Transactions on Information Technology in Biomedicine, 2011, 15(6): 918-928.
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