한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제23권4호
  • /
  • Pages.51-56
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  • 2018
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Image Enhancement Method using Canny Algorithm based on Curvelet Transform

  • Mun, Byeong-Cheol (Dept. of Avionics Control System, Korea Polytechnics Aviation College)
  • 투고 : 2018.04.03
  • 심사 : 2018.04.10
  • 발행 : 2018.04.30
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초록

This paper proposes the efficient preprocessing method based on curvelet transform for edge enhancement in image. The propose method is generated the edge map by using the Canny algorithm to wavelet transform, which is the sub-step of the curvelet transform. In order to improve the part of edge feature, the selective sharpening according to the generate edge map is applied. In experimental result, the propose method achieves that the enhancement of edge feature is better than conventional methods. This leads that peak to signal noise ratio, edge intensity are improvement on average about 1.92, 1.12dB respectively.

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참고문헌

  1. M. Deepa, "Wavelet and Curvelet based Thresholding Techniques for Image Denoising," International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE), Volume 1, Issue 10, December (2012).
  2. M. Shukla and S. Changlani, "A Comparative Study of Wavelet and Curvelet Transform for Image Denoising," IOSR Journal of Electronics and Communication Engineering(IOSR-JECE), Volume 7, Issue 4, PP 63-68, Oct. (2013). https://doi.org/10.9790/2834-0746368
  3. Y. Shen, Q. Liu, S. Lou, and Y.L. Hou "Wavelet-Based Total Variation and Nonlocal Similarity Model for Image Denoising," IEEE Signal Processing Letters, 2017, Volume 24, Issue 6, June (2017).
  4. JH Li, YJ Zhang, R Qi, and QH Liu, "Wavelet-Based Higher Order Correlative Stacking for Seismic Data Denoising in the Curvelet Domain," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Volume: PP, Issue: 99, April (2017). https://doi.org/10.1109/JSTARS.2017.2685628
  5. V. Vijay Kumar Raju, M. Prema Kumar, "Denoising of MRI and X-Ray images using Dual Tree Complex Wavelet and Curve let Transforms," IEEE International Conference on Communication and Signal Processing, April (2014).
  6. R.A. Ansari, and K.M. Buddhiraju "NOISE FILTERING OF REMOTELY SENSED IMAGES USING HYBRID WAVELET AND CURVELET TRANSFORM APPROACH," Geoscience and Remote Sensing Symposium(IGARSS), 2015 IEEE International, July (2015).
  7. E. J. Candes and D. L. Donoho, "Curvelets," [Online] Available: http://www-stat.stanford.edu/-donoho/Reports/1999/curvelets.pdf, (1999).
  8. J. L. Starck, E. J. Candes, and D. L. Donoho, "The curvelet transform for image denoising," IEEE Trans. Image Process., vol. 11, pp. 670-684, (2002). https://doi.org/10.1109/TIP.2002.1014998
  9. M. Kalyan and K. Sekhar, "Discrete Curvelet and Morphological Based Adaptive Satellite Image Enhancement," Global Journal of Advanced Engineering Technologies, Vol3, Issue 3, (2014).
  10. K. Jemseera, and P. Noufal "Satellite Image Fusion Based on Improved Fast Discrete Curvelet Transforms," Advances in Computing and Communications(ICACC), 2015 Fifth International Conference on, Sept. (2015).
  11. Anil A. Patil and Jyoti Singhai, "Image denoising using curvelet transform: an approach for edge preservation," CSIR, JSIR Vol.69(01), Jan. (2010).
  12. T. Qiao, J. Ren, Z. Wang, J. Zabalza, M. Sun, H. Zhao, S. Li, J. A. Benediktsson, Q. Dai, and S. Marshall, "Effective Denoising and Classification of Hyperspectral Images Using Curvelet Transform and Singular Spectrum Analysis," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 1, pp. 119-133, JANUARY (2016). https://doi.org/10.1109/TGRS.2016.2598065
  13. Mahdad Esmaeili, Alireza Mehri Dehnavi, Hossein Rabbani, and Fedra Hajizadeh, "Speckle Noise Reduction in Optical Coherence Tomography Using Twodimensional Curvelet-based Dictionary Learning," Journal of Medical Signals & Sensors, Vol 7, No 2, Jun. (2017).
  14. K Wu, X Zhang,and M Ding, "Curvelet based nonlocal means algorithm for image denoising," AEU-Internatio nal Journal of Electronics and Communications, Volume 68, Issue 1, Page 37-43, January (2014). https://doi.org/10.1016/j.aeue.2013.07.011
  15. Bao, Paul, Lei Zhang, and Xiaolin Wu. "Canny edge detection enhancement by scale multiplication." IEEE transactions on pattern analysis and machine intelligence 27.9: 1485-1490 (2005). https://doi.org/10.1109/TPAMI.2005.173
  16. Rong, Weibin, et al. "An improved CANNY edge detection algorithm." Mechatronics and Automation (ICMA), 2014 IEEE International Conference on. IEEE, (2014).
  17. A. Ein-shoka, H. Kelash, O. Faragallah, and H. El-sayed, "Enhancement of IR Images using Homomorphic Filtering in Fast Discrete Curvelet Transform (FDCT)," International Journal of Computer Applications(0975 - 8887) Volume 96, No.8, (2014).
  18. Anilet Bala, Chiranjeeb Hati and CH Punith, "Image Denoising Method Using curvelet Transform and Wiener Filter," International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 3, Issue 1, January (2014).