Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on 8-Directional Complex Wavelet Transform for Efficient Image Processing

효율적인 영상처리를 위한 8방향 컴플렉스 웨이브렛 변환에 관한 연구

  • Shin, Seong (Department of Electronic Engineering, Wonkwang University) ;
  • Moon, Sung Ryong (Department of Electronic Engineering, Wonkwang University)
  • 신성 (원광대학교 전자공학과) ;
  • 문성룡 (원광대학교 전자공학과)
  • Received : 2012.11.15
  • Published : 2013.03.25
Download PDF
⟨ Previous Next ⟩

Abstract

This paper is a study on Dual Tree Complex Wavelet Transform, which improved directional information for efficient image processing. Dual Tree Complex Wavelet Transform satisfies characteristics of shift invariance, and includes 6 directional information, which is more than previous Discrete Wavelet Transform. However, in images of buildings, there are many horizontal and vertical edge components. Therefore, all the high-frequency components of image are not expressed by 6 directional information subbands. This paper proposes 8-directional Complex Wavelet Transform with excellent high-frequency separation features by creating horizontal vertical($0^{\circ}$, $90^{\circ}$) subband besides 6 directional information subband of previous Dual Tree Complex Wavelet Transform. The proposed method can create and combine various directional information subbands according to features of image. Performance is evaluated by applying the method to noise removal.

본 논문은 효율적인 영상처리를 위해 방향성 정보를 개선한 이중 트리 컴플렉스 웨이브렛에 관한 연구이다. 이중 트리 컴플렉스 웨이브렛 변환은 이동 불변 성질을 만족하며, 기존 이산 웨이브렛 보다 많은 6개의 방향성 정보를 포함한다. 하지만 간판, 건물과 같은 구조물의 경우 수평 수직 방향 에지 성분들이 많이 포함되어 있어서 6개의 방향성 부대역으로만 영상의 고주파 성분을 모두 표현하기에는 부족하다. 따라서 기존 이중 트리 컴플렉스 웨이브렛 변환의 6개 방향성 부대역 외에 수직 수평($0^{\circ}$, $90^{\circ}$) 부대역을 생성함으로써 우수한 고주파 분리 특성을 갖는 8방향 컴플렉스 웨이브렛 변환 방법을 제안한다. 본 논문에서는 영상의 특성에 따라 다양한 방향성 성분 부대역 생성이 가능하며, 대표적 응용분야인 잡음제거에 활용해 봄으로써 성능을 평가한다.

Keywords

References

  1. I. Daubechies, "The Wavelet Transform, Time- Frequency Localisation and Signal Analysis," IEEE Trans. Information Theory, Vol. 36, No. 5, pp. 961-1005, Sep. 1995.
  2. I. Daubechies. "Orthonormal bases of compactly supported wavelets," Commun. Pure and Appl. Math., pp. 909-996, Oct. 1988.
  3. S. Mallat, "A Theory for Multiresolution Signal Decomposition: The Wavelet Representation," IEEE Patt. Analysis and Machine Intelligence, Vol. 11, No. 7, pp. 674-693, 1989. https://doi.org/10.1109/34.192463
  4. N. Kingsbury. "Shift invariant properties of the dual-tree complex wavelet transform," IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), Vol. 3, pp. 1221-1224, 1999
  5. N. Kingsbury. "Complex wavelets and shift invariance," IEEE Seminar. Timescale and Time-Frequency Analysis and Applications, pp. 501-510, 2000.
  6. N. Kingsbury. "A dual-tree complex wavelet transform with improved orthogonality and symmetry properties," IEEE Int. Conf. Image Processing, Vol. 1429, Vancouver, Sep. 2000.
  7. N. Kingsbury, "Complex wavelets for shift invariant analysis and filtering of signals," Appl. Comput. Harmon. Anal., vol. 10, No. 3, pp. 234-253, May 2001. https://doi.org/10.1006/acha.2000.0343
  8. 임중희, 지인호, "비분리 영상처리에서 이중 트리웨이브렛 변환을 사용한 디지털 영상 성능 개선에 관한 연구," 한국인터넷방송통신학회 논문지, 제12권, 제1호, pp. 65-74, 2012.
  9. 엄일규, 김유신, "복소수 웨이블릿과 베르누이-가우스 모델을 이용한 잡음 제거," 대한전자공학회논문지, 제43권 SP편, 제5호, pp. 52-61, 2006.
  10. J. Yang and Y. Wang, "Image and Video Denoising Using Adaptive Dual-Tree Discrete Wavelet Packets," IEEE Trans. on CSVT, Vol. 19, No. 5, 2009.
  11. K. J. Priya and R. S. Rajesh, "Local Statistical Features of Dual-Tree Complex Wavelet Transform on Parallelogram Image Structure for Face Recognition with Single Sample," Proceedings of the Int. Conf. on Recent Trends in ITC, pp. 50-54, 2010.
  12. R. M. Rao and A. S. Bopardikar, Wavelet Transforms: Introduction to Theory and Applications. Addison Wesley, 1998.
  13. C. S. Burrus, R. A. Gopinath and H. Guo, Introduction to Wavelet and Wavelet Transforms: A Primer. Prentice Hall, 1998.
  14. B. Jahne, Practical handbook on image processing for scientific and technical applications, 2nd ed. CRC Press, 2004.
  15. Q. Pan, L. Zhang, G. Dai and H. Zhang, "Two Denoising Methods by Wavelet Transform," IEEE Trans. Signal Processing, Vol. 47, pp. 3401-3406. Dec. 1999. https://doi.org/10.1109/78.806084
  16. D. L. Donoho and I. M. Johnstone, "Adapting to Unknown Smoothness via Wavelet Shrinkage," J. Amer. Statist. Assoc., Vol. 90, pp. 1200-1224, Dec. 1995. https://doi.org/10.1080/01621459.1995.10476626
  17. L. Sendur and I. Selesnick, "Bivariate shrinkage functions for wavelet based denoising exploiting interscale dependency," IEEE Trans. Signal Processing, pp. 2744-2756, Nov. 2002.