Restoration and fusion optimization scheme of multifocus image using genetic search strategies

• Autorzy: Zhang X. , Han J. , Liu P.
• Języki publikacji: EN

Abstrakty

EN

A novel and optimal algorithm is presented that is suitable for multifocus image fusion. A synergistic combination of segmentation techniques and genetic search strategies is employed in salience analysis of contrast feature-vision system. Some evaluation measures are suggested and applied to compare the performance of different fusion schemes. Two cases of the generated test images are discussed and extensive experiments demonstrate that in one case most fused images achieve reconstruction or optimized effects with respect to the reference image when the focus objectives are not overlapped blurred, and in the other case this method produces better results outperforming other conventional methods when the focus objectives are overlapped blurred. It is therefore shown that the performance of the fusion algorithm proposed optimizes further the fused image globally accomplishing absolute restoration or optimized fusion of multifocus image to the reference image. This algorithm is also suitable for the digital camera images of real scene and gets to be optimized well. s. 927-942, bibliogr. 21 poz..

Słowa kluczowe

EN

multifocus image fusion; genetic search strategies; zero reconstruction error; contrast feature-vision system

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2005
• Tom: Vol. 35, nr 4
• Strony: 927--942
• Opis fizyczny: Bibliogr. 21 poz.

Twórcy

autor

Zhang X.

• School of Electronics and Information Engineering's, Xi'an Jiaotong University, 28 Xian'ning West Road, Xi'an 710049, People's Republic of China

autor

Han J.

• School of Electronics and Information Engineering's, Xi'an Jiaotong University, 28 Xian'ning West Road, Xi'an 710049, People's Republic of China

autor

Liu P.

• School of Electronics and Information Engineering's, Xi'an Jiaotong University, 28 Xian'ning West Road, Xi'an 710049, People's Republic of China

Bibliografia

• [1] Varshney P.K., Multisensor data fusion, Electronics and Communication Engineering Journal 9(6), 1997, pp. 245-53.
• [2] Burt P.J., Andelson E.H., The Laplacian pyramid as a compact image code, IEEE Transactions on Communication COM-31(4), 1983, pp. 532-40.
• [3] Burt P.J., Adelson E.H., Merging images through pattern decomposition, Proceedings of the SPIE 575, 1985, pp. 173-81.
• [4] Toet a. , van Ruyven L . J., Valeton J.M. , Merging thermal and visual images by a contrast pyramid, Optical Engineering 28(7), 1989, pp. 789-92.
• [5] Toet A., Multiscale contrast enhancement with applications to image fusion, Optical Engineering 31(5), 1992, pp. 1026-31.
• [6] Burt P.J., Kolczynski R.J., Enhanced image capture through fusion, [In] Proceedings of the 4th International Conference on Computer Vision 4, Berlin, Germany 1993, pp. 173-82.
• [7] Liu Z., Tsukada K., Hanasaki K., Ho Y.K., Dai Y.P., Image fusion by using steerable pyramid, Pattern Recognition Letters 22(9), 2001, pp. 929-39.
• [8] Daubechies I., Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics/ CBMS-NSF regional conference series in applied mathematics 61, Philadelphia 1992.
• [9] Huntsberger T., Jawerth B., Wavelet based sensor fusion, Proceedings of the SPIE 2059, 1993, pp. 488-98.
• [10] Mallat S.G., A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7), 1989, pp. 674-93.
• [11] Mallat S.G., Wavelets for a vision, Proceedings of the IEEE 84(4), 1996, pp. 604-14.
• [12] Zhang Z., Blum R.S., A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application, Proceedings of the IEEE 87(8), 1999, pp. 1315-26.
• [13] Chibani Y., Houacine A., On the use of redundant wavelet transform for multisensor image fusion, [In] Proceedings of the 7th IEEE International Conference on Electronics, Circuits and Systems, Jounieh, Lebanon 2000, pp. 442-5.
• [14] Li S.T., Wang Y.N., Zhang C.F., Feature of human vision system based multi-focus image fusion, Acta Electronica Sinica 29(12), 2001, pp. 1699-701.
• [15] Li S.T., Kwok J.T., Wang Y.N., Multifocus image fusion using artificial neural networks, Pattern Recognition Letters 23(8), 2002, pp. 985-97.
• [16] Huang J.W., Shi Y.Q., Dai X.H., A segmentation-based image coding algorithm using the features of human vision system, Journal of Image and Graphics 4(5), 1999, pp. 400-4.
• [17] Goldberg D.E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison -Wesley Publishing Company, Redwood City, CA 1989.
• [18] de Peufeilhoux R., Genetic fusion of registered images, Proceedings of the SPIE 1607, 1991, pp. 380-4.
• [19] Xu Y.L., Bi D.Y., Mao B.X., Ma L.H., A genetic search algorithm for motion estimation, Journal of Image and Graphics 6(2), 2001, pp. 164-7.
• [20] Maes F., Collignon A., Vandermeulen D., Marchal G., Suetens P., Multimodality image registration by maximization of mutual information, IEEE Transactions on Medical Imaging 16(2), 1997, pp. 187-98.
• [21] Li H., Manjunath B.S., Mitra S.K., Multisensor image fusion using the wavelet transform, Graphical Models and Image Processing 57(3), 1995, pp. 235-45.