방향성 다해상도 변환을 사용한 새로운 다중초점 이미지 융합 기법

A Novel Multi-focus Image Fusion Technique Using Directional Multiresolution Transform

  • 투고 : 2009.06.18
  • 발행 : 2009.08.30

초록

본 논문은 최근 소개된 curvelet 변환 구성을 사용하여 하잇브리드 다초점 이미지 융합 기법을 다룬다. 하잇브리화는 MS 융합 규칙을 새로운 "복제" 방법과 결합시킴으로써 얻어진다. 제안된 기법은 MS 규칙을 사용하여 각 분해 레벨 이미지의 스펙트럼내에 m개의 가장 두드러진 항들만을 융합시킨다. 이 기법은 이미지의 어떠한 스케일과 방향, 이동에서 변환 집합의 MSC에 충실하여 m-항 융합으로 합성이 이루어진다. 제안한 방법을 평가하기 위하여 Xydeas 와 Petrovic이 제안한 경계선에 민감한 객관적 품질 척도를 적용하였다. 실험 결과는 제안한 기법이 잉여, 쉬프트-불변 Dual-Tree 복소수 웨이블릿 변환에 대한 대안으로서의 가능성을 보여주었다. 특히, 50%의 m-항 융합은 어떤 시각적인 품질 저하를 갖지 않는 결과를 주는 것이 확인되었다.

This paper addresses a hybrid multi-focus image fusion scheme using the recent curvelet transform constructions. Hybridization is obtained by combining the MS fusion rule with a novel "copy" method. The proposed scheme use MS rule to fuse the m most significant terms in spectrum of an image at each decomposition level. The scheme is dubbed in this work as m-term fusion in adherence to its use of the MSC (most significant coefficients) in the transform set at any given scale, orientation, and translation. We applied the edge-sensitive objective quality measure proposed by Xydeas and Petrovic to evaluate the method. Experimental results show that the proposed scheme is a potential alternative to the redundant, shift-invariant Dual-Tree Complex Wavelet transforms. In particular, it was confirmed that a 50% m-term fusion produces outputs with no visible quality degradation.

키워드

참고문헌

  1. Raskar, R., Ilie, A., and Yu, J., "Image Fusion for Context Enhancement. and Video Surrealism", Proc 3rd International Symposium on Non-Photorealistic Animation and Rendering (NPAR) 2004, Annecy, France
  2. Rockinger, O. "Pixel-Level Fusion of Image Sequences Using Wavelet Frames", In Mardia, K. V., Gill, C.A., and Dryden, I.L., eds., Proc 16th Leeds Applied Share Research Workshop. Leeds University Press, pp. 149-154, 1996
  3. Heizmann, M., "Image Fusion Tutorial", Lecture Notes in IEEE Int'l Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), Heidelberg, Germany, Sept 3, 2006
  4. Nikolov, S. G., Lewis, J.J., O'Callaghan, R.J., Bull, D.R., and Canagarajah, C.N., "Hybrid Fused Displays: Between Pixel and Region-based Image Fusion", Proc 7th International Conference on Information Fusion, Stockholm, Sweden, pp. 1072-1079, June 2004
  5. Ramac, L. C., Uner, M. K., Varshney, P. K., "Morphological filters and wavelet based image fusion for concealed weapon detection," Proceedings of SPIE, vol. 3376, 1998
  6. Xydeas, C., and Petrovic, V., "Objective image fusion performance measure", Electronic Letters, 36(4):308-309, February 2000 https://doi.org/10.1049/el:20000267
  7. Daubechies, I., and Sweldens, W., "Factoring Wavelet Transforms into Lifting Steps", J. Fourier Anal. Appl., Vol. 4, No.3, pp. 247-269, 1998 https://doi.org/10.1007/BF02476026
  8. Mallat, S., and Hwang, W.L., "Singularity detection and processing with wavelets", IEEE Transactions on Information Theory, Special Issue on Wavelet Transforms and Multiresolution Signal Analysis, vol. 38, no.2, pp. 617-643, March 1992
  9. Candes, E., "What is a Curvelet", Notice of the AMS, vol.50, number 11, pp. 1402-1403, Dec. 2003
  10. Gyaourova, A., Kamath, C., and Fodor, I.K., "Undecimated wavelet transforms for image de-nosing", Center for Applied Sci. Computing, Lawrence Livermore Natl. Lab. [online], Available from www.IInl.gov/casc/sapphire/pubs/150931.pdf [Accessed June 10, 2008].
  11. Selesnick, I. W., "A Higher-Density Discrete Wavelet Transform", IEEE Transactions on Signal Processing, vol 54, issue 8, pp 3039-3048, Aug. 2006 https://doi.org/10.1109/TSP.2006.875388
  12. Selesnick, I. W., The double-density dual-tree DWT. "IEEE Trans. on Signal Processing", 52(5):1304-1314, May 2004 https://doi.org/10.1109/TSP.2004.826174
  13. Sweldens, W., "The Lifting Scheme: A new philosophy in biorthogonal wavelet constructions", In A. F. Laine and M. Unser, editors, Wavelet Applications in Signal and Image Processing III, pp. 68-79, Proc. SPIE 2569, 1995
  14. Candes, E.J., and Donoho, D.L., "Curvelets--a surprisingly effective nonadaptive representation for objects with edges", in: C.R.A. Cohen, L. Schumaker (Eds.), Curve and Surface Fitting: Saint Malo 1999, Vanderbilt University Press, Nashville, TN, 2000
  15. Candes, E.J., Demanet, L., Donoho, D.L., and Ying, L., "Fast Discrete Curvelet Transforms", Multiscale Modeling & Simulation, vol 5, issue 3, pp. 861-899, 2006 https://doi.org/10.1137/05064182X