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SCIENTIA SINICA Informationis, Volume 48, Issue 8: 1000-1021(2018) https://doi.org/10.1360/N112017-00085

Multi-focus image fusion method based on discrete Tchebichef transform and focus measure

More info
  • ReceivedAug 28, 2017
  • AcceptedOct 18, 2017
  • PublishedFeb 1, 2018

Abstract

Transform-based image fusion methods are widely used in multi-focus image fusion owing to their promising fusion effect, and their noise robustness. However, the time complexity of conventional transform-based image fusion methods is generally high. In this paper, a multi-focus image fusion method based on the discrete Tchebichef transform (DTT) and a focus measure is proposed. According to the relationship between DTT and a correlation analysis, the focus measure of image blocks in source images can be evaluated by limited low-order DTT coefficients. Hence, the source images are fused by the principle of maximum focus measure. Our experimental results show that the proposed method can reduce the fusion time while ensuring the fusion effect, and exhibit high noise robustness during image fusion.


Funded by

国家自然科学基金(61572092)

国家自然科学基金—广东联合基金(U1401252)

国家重点研发计划(2016YFC1000307-3)


References

[1] Wan T, Zhu C, Qin Z. Multifocus image fusion based on robust principal component analysis. Pattern Recognition Lett, 2013, 34: 1001-1008 CrossRef Google Scholar

[2] Li S, Kang X, Fang L. Pixel-level image fusion: A survey of the state of the art. Inf Fusion, 2017, 33: 100-112 CrossRef Google Scholar

[3] Kumar M, Dass S. A Total Variation-Based Algorithm for Pixel-Level Image Fusion. IEEE Trans Image Process, 2009, 18: 2137-2143 CrossRef PubMed ADS Google Scholar

[4] Yan X, Kang W, Deng F. Palm vein recognition based on multi-sampling and feature-level fusion. Neurocomputing, 2015, 151: 798-807 CrossRef Google Scholar

[5] Lan X, Ma A J, Yuen P C. Joint Sparse Representation and Robust Feature-Level Fusion for Multi-Cue Visual Tracking. IEEE Trans Image Process, 2015, 24: 5826-5841 CrossRef PubMed ADS Google Scholar

[6] Huang Z H, Li W J, Wang J. Face recognition based on pixel-level and feature-level fusion of the top-level's wavelet sub-bands. Inf Fusion, 2015, 22: 95-104 CrossRef Google Scholar

[7] Heideklang R, Shokouhi P. Decision-Level Fusion of Spatially Scattered Multi-Modal Data for Nondestructive Inspection of Surface Defects.. Sensors, 2016, 16: 105-126 CrossRef PubMed Google Scholar

[8] Sun B, Li L, Wu X. Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild. J Multimodal User Interfaces, 2016, 10: 125-137 CrossRef Google Scholar

[9] Wei C Y, Zhou B Y, Guo W. Novel fusion rules for transform-domain image fusion methods. In: Proceedings of International Conference on Mechatronics and Industrial Informatics, Toronto, 2015. 846--850. Google Scholar

[10] Liu Y, Liu S, Wang Z. Multi-focus image fusion with dense SIFT. Inf Fusion, 2015, 23: 139-155 CrossRef Google Scholar

[11] Wang Z, Ma Y, Gu J. Multi-focus image fusion using PCNN. Pattern Recognition, 2010, 43: 2003-2016 CrossRef Google Scholar

[12] Toet A. Image fusion by a ratio of low-pass pyramid. Pattern Recognition Lett, 1989, 9: 245-253 CrossRef Google Scholar

[13] Tang J. A contrast based image fusion technique in the DCT domain. Digital Signal Processing, 2004, 14: 218-226 CrossRef Google Scholar

[14] Zhang H X, Cao X. A way of image fusion based on wavelet transform. In: Proceedings of the 9th International Conference on Mobile Ad-hoc and Sensor Networks, Dalian, 2013. 498--501. Google Scholar

[15] Khare A, Srivastava R, Singh R. Edge preserving image fusion based on contourlet transform. In: Proceedings of International Conference on Image and Signal Processing, Agadir, 2012. 93--102. Google Scholar

[16] Xiao B, Lu G, Zhang Y. Lossless image compression based on integer Discrete Tchebichef Transform. Neurocomputing, 2016, 214: 587-593 CrossRef Google Scholar

[17] Huang W, Jing Z. Evaluation of focus measures in multi-focus image fusion. Pattern Recognition Lett, 2007, 28: 493-500 CrossRef Google Scholar

[18] Yap P T, Raveendran P. Image focus measure based on Chebyshev moments. IEE Proc Vis Image Process, 2004, 151: 128-136 CrossRef Google Scholar

[19] Thelen A, Frey S, Hirsch S. Improvements in Shape-From-Focus for Holographic Reconstructions With Regard to Focus Operators, Neighborhood-Size, and Height Value Interpolation. IEEE Trans Image Process, 2009, 18: 151-157 CrossRef PubMed ADS Google Scholar

[20] Yang G, Nelson B. Wavelet-based autofocusing and unsupervised segmentation of microscopic images. In: Proceedings of IEEE International Conference on Intelligent Robert and Systems, Las Vegas, 2003. 2143--2148. Google Scholar

[21] Santos A, Ortiz De Solórzano C, Vaquero J J. Evaluation of autofocus functions in molecular cytogenetic analysis. J Microsc, 1997, 188: 264-272 CrossRef Google Scholar

[22] Eskicioglu A M, Fisher P S. Image quality measures and their performance. IEEE Trans Commun, 1995, 43: 2959-2965 CrossRef Google Scholar

[23] Cao L, Jin L X, Tao H J, et al. Multi-focus image fusion based on spatial frequency in discrete cosine transform domain. IEEE Signal Process Lett, 2014, 22: 220--224. Google Scholar

[24] Liu Z, Blasch E, Xue Z. Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study.. IEEE Trans Pattern Anal Mach Intell, 2012, 34: 94-109 CrossRef PubMed Google Scholar

[25] Qu G, Zhang D, Yan P. Information measure for performance of image fusion. Electron Lett, 2002, 38: 313-315 CrossRef Google Scholar

[26] Zhao J Y, Laganiere R, Liu Z. Performance assessment of combinative pixel-level image fusion based on an absolute feature measurement. Int J Innov Comput Inf Control, 2006, 3: 1433--1447. Google Scholar

[27] Piella G, Heijmans H. A new quality metric for image fusion. In: Proceedings of International Conference on Image Processing, Barcelona, 2003. 173--176. Google Scholar

[28] Xydeas C S, Petrovic V. Objective image fusion performance measure. Mil Tech Courier, 2000, 36: 308--309. Google Scholar

[29] Li S, Kang X, Hu J. Image matting for fusion of multi-focus images in dynamic scenes. Inf Fusion, 2013, 14: 147-162 CrossRef Google Scholar

[30] Shutao Li , Xudong Kang , Jianwen Hu . Image Fusion With Guided Filtering. IEEE Trans Image Process, 2013, 22: 2864-2875 CrossRef PubMed ADS Google Scholar

[31] Tang J. A contrast based image fusion technique in the DCT domain. Digital Signal Processing, 2004, 14: 218-226 CrossRef Google Scholar

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