Denoising of Infrared Images by an Adaptive Threshold Method in the Wavelet Transformed Domain

웨이브렛 변환 영역에서 적응문턱값을 이용한 적외선영상의 잡음제거

  • Cho, Chang-Ho (IT Development team, R&D Center, CESCO Co. Ltd.) ;
  • Lee, Sang-Hyo (Dept. of Information & Control Engineering, Kwangwoon university) ;
  • Lee, Jong-Yong (Dept. of general education, Kwangwoon university) ;
  • Cho, Do-Hyeon (Dept. of Digital Electronics & Information, Inha Collage) ;
  • Lee, Sang-Chuel (Dept. of micro-robot, Jaineung College)
  • 조창호 ((주)세스코 기술연구소 IT개발팀) ;
  • 이상효 (광운대학교 정보제어공학과) ;
  • 이종용 (광운대학교 교양학부) ;
  • 조도현 (인하공업전문대학 디지털전자정보과) ;
  • 이상철 (재능대학 마이크로로봇과)
  • Published : 2006.12.25

Abstract

This thesis deals with a wavelet-based method of denoising of infrared images contaminated with impulse noise and Gaussian noise, he method of thresholding the wavelet coefficients using derivatives and median absolute deviations of the wavelet coefficients of the detail subbands was proposed to effectively denoise infrared images with noises. Particularly, in order to eliminate the impulse noise the method of generating binary masks indicating locations of the impulse noise was selected. By this method, the threshold values dividing edges and noises were obtained more effectively proving the validity of the denoising method compared with the conventional wavelet shrinkage method.

본 연구에서는, 열상장비(thermal imaging equipment)로 촬영한 적외선 영상의 화질을 저해하는 주된 요소인 임펄스 잡음(impulse noise)과 가우시안 잡음(Gaussian noise)을 제거하는 웨이브렛 변환 기반 방법을 논의한다. 효과적인 잡음제거를 위하여 잡음으로 손상된 적외선 영상에 대하여 상세 부분대역 웨이브렛 계수에 대한 미분과 중앙절대편차(median absolute deviation)를 이용한 문턱값 설정방법을 제안하였다. 특히, 임펄스성 잡음제거를 위해서 웨이브렛 계수를 미분하여 임펄스 잡음의 위치를 나타내는 이진 마스크를 생성하는 방법을 채택하였다. 이와 같은 방법에 의해, 모서리와 잡음을 구분하는 적응 문턱 값 설정을 보다 효율적으로 얻을 수 있었고, 기존 웨이브렛 수축법과 비교를 통하여 제안한 잡음제거 방법의 타당성을 확인하였다.

Keywords

References

  1. P. Sayood, Intriduction to Data Compression, Morgan Kaufmann, 1993
  2. C. S. Burrus, R. A. Gopinath, H. Guo, Introduction to Wavelets and Wavelet Transforms A Primer, Prentice-Hall, 1998
  3. A. Chambolle, R. A. DeVore, N. Lee, and B. J. Lucier, 'Nonlinear Wavelet Image Processing: variational Problems, Compression, and Noise Removal Through Wavelet Shrinkage', IEEE Trans. on Image Processing, 7(3), pp. 319-335, 1998 https://doi.org/10.1109/83.661182
  4. D. L. Donoho and I.M. Johnstone, 'Ideal Spatial Adaptation via Wavelet Shrinkage', Jour. of the Amer. Stat. Asso., 90(432), pp. 1200-1224, 1995 https://doi.org/10.2307/2291512
  5. D. L. Donoho and I. M. Johnstone, 'Ideal Spatial Adaptation Via Wavelet Shrinkage', Biometrika, 81, pp. 425-455, 1994 https://doi.org/10.1093/biomet/81.3.425
  6. R. N. Strickland, H. I. Hahn, 'Wavelet Methods for Extracting Objects from Complex Backgrounds', IEEE ICASSP, Albuquereque, USA, pp.2997-2300, April 1996
  7. Z. Li, Z. K Shen, 'An Estimation Method Based on Wavelet Transformation for Infrared Image Noise', IEEE ICASSP, Albuquereque, USA, pp.2997 - 2300, April 1996
  8. S. Friba, M. Boulemden, 'Meteorological Image Processing with Wavelets', IEEE ICASSP, Albuquereque, USA, pp.2997-2300, April 1998
  9. J. Morlet, C. H. Chen, ed., 'Sampling Theory and Wave Propagation, In NATO ASI series, Vol. 1, Issues in Acoustic Signal/lmage Processing and Recognition', Springer- Verlag, Berlin, pp. 233-261, 1983
  10. A. Grossmann and J. Morlet, 'Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape', SIAM J. Math. Anal., pp. 723-736, 1984
  11. A. Croissier, D. Esteban, and C. Galand, 'Perfect Channel Splitting by Use of Interpolation/ Decomposition/Tree Decomposition Techniques', In Int. Conf. on Info, Sciences and Systems, pp. 447 -446, 1976
  12. P. Burt and E. Adelson, 'The Laplacian Pyramid as a Compact Image Code', IEEE trans. Comm., 31, pp. 482-540, 1983
  13. S. Mallat, 'Multiresolution Approximations and Wavelet Or-thonormal Bases of L2(R)', Trans. Amer. Math. Soc., pp. 69-87, 1989
  14. A. Haar, 'Zur Theone der Orthogonalen Funktionen-Systeme', Math Annal., pp. 331-371, 1910
  15. I. Daubechies, 'Orthonormal Bases of Compactly Supported Wavelets', Commun. on Pure and Appl. Math., 41(2), pp. 909-996, 1988 https://doi.org/10.1002/cpa.3160410705
  16. A. Cohen and I. Daubechies, and J. -C. Feuveau, 'Biorthogonal Bases of Compactly Supported Wavelets', Commun. Pure and Appl. Math., 45, pp. 485-560, 1992 https://doi.org/10.1002/cpa.3160450502
  17. R. Coifman, Y. Meyer, and M. Wickerhauser, 'Wavelet Analysis and Signal Processing, In wavelet and their Applications', B. Ruskai et al. eds., jones and Barlett Pub., Boston, pp. 153-178, 1992