Multi-focus Image Fusion Based on Hess Matrix

XIAO Bin; TANG Han; XU Yunqiu; LI Weisheng

doi:10.11999/JEIT170497

Volume 40 Issue 2

Feb. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(2): 255-263

XIAO Bin, TANG Han, XU Yunqiu, LI Weisheng. Multi-focus Image Fusion Based on Hess Matrix[J]. Journal of Electronics & Information Technology, 2018, 40(2): 255-263. doi: 10.11999/JEIT170497

Citation:

XIAO Bin, TANG Han, XU Yunqiu, LI Weisheng. Multi-focus Image Fusion Based on Hess Matrix[J]. Journal of Electronics & Information Technology, 2018, 40(2): 255-263. doi: 10.11999/JEIT170497

XIAO Bin, TANG Han, XU Yunqiu, LI Weisheng. Multi-focus Image Fusion Based on Hess Matrix[J]. Journal of Electronics & Information Technology, 2018, 40(2): 255-263. doi: 10.11999/JEIT170497

Citation:

XIAO Bin, TANG Han, XU Yunqiu, LI Weisheng. Multi-focus Image Fusion Based on Hess Matrix[J]. Journal of Electronics & Information Technology, 2018, 40(2): 255-263. doi: 10.11999/JEIT170497

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Multi-focus Image Fusion Based on Hess Matrix

doi: 10.11999/JEIT170497 cstr: 32379.14.JEIT170497

Funds:

The National Natural Science Foundation of China (61572092, U1401252), The National Science and Technology Major Project (2016YFC1000307-3)

Received Date: 2017-05-24
Rev Recd Date: 2017-10-18
Publish Date: 2018-02-19

Abstract

Abstract

This paper proposes a Hess (also known as Hessian) matrix-based multi-focus image fusion method. In this method, multi-scale Hess matrix is utilized to detect feature and background regions. On this basis, source images are split into two different parts, and different fusion strategies are applied to generating decision map respectively. By combining decision maps in different parts, an initial decision map is obtained, and then the initial decision map is refined with post-processing method. To improve the performance of the fusion method, a new focus measure is proposed based on multi-scale Hess matrix for both feature and background regions. Integral images are also introduced for fast computation to meet the real-time application requirements. Experimental results demonstrate that the proposed method is competitive with or even outperforms the state-of-the-art methods in terms of both subjective visual perception and objective evaluation metrics.
- Multi-focus image fusion,
- Hess matrix,
- Integral images

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References(17)

References

LI H, LI X, YU Z, et al. Multifocus image fusion by combining with mixed-order structure tensors and multiscale neighborhood[J]. Information Sciences An International Journal, 2016, s349(C): 25-49. doi: 10.1016/j.ins.2016.02.030.

BAI X, ZHANG Y, ZHOU F, et al. Quadtree-based multi- focus image fusion using a weighted focus-measure[J]. Information Fusion, 2015, 22: 105-118. doi: 10.1016/j.inffus. 2014.05.003.

PETROVIC V S and XYDEAS C S. Gradient-based multiresolution image fusion[J]. IEEE Transactions on Image Processing, 2004, 13(2): 228-237. doi: 10.1109/TIP.2004. 823821.

LEWIS J J, OCALLAGHAN R J, NIKOLOV S G, et al. Pixel-and region-based image fusion with complex wavelets[J]. Information Fusion, 2007, 8(2): 119-130. doi: 10.1016/j.inffus. 2005.09.006.

LI S, KANG X, FANG L, et al. Pixel-level image fusion: A survey of the state of the art[J]. Information Fusion, 2017, 33: 100-112. doi: 10.1016/j.inffus.2016.05.004.

LIU Y, LIU S, and WANG Z. Multi-focus image fusion with dense SIFT[J]. Information Fusion, 2015, 23(C): 139-155. doi: 10.1016/j.inffus.2014.05.004.

LI S, KANG X, and HU J. Image fusion with guided filtering[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2864-2875. doi: 10.1109/TIP.2013.2244222.

LI S, KANG X, HU J, et al. Image matting for fusion of multi-focus images in dynamic scenes[J]. Information Fusion, 2013, 14(2): 147-162. doi: 10.1016/j.inffus.2011.07.001.

ZHOU Z, LI S, and WANG B. Multi-scale weighted gradient- based fusion for multi-focus images[J]. Information Fusion, 2014, 20(1): 60-72. doi: 10.1016/j.inffus.2013.11.005.

WANG Z, MA Y, and GU J. Multi-focus image fusion using PCNN[J]. Pattern Recognition, 2010, 43(6): 2003-2016. doi: 10.1016/j.patcog.2010.01.011.

LOWE and DAVID G. Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

BAY H, ESS A, and TUYTELAARS T. Speeded-up robust features[J]. Computer Vision Image Understanding, 2008, 110(3): 404-417.

ZHANG Y, BAI X, and WANG T. Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure[J]. Information Fusion, 2017, 35: 81-101. doi: 10.1016/j.inffus.2016.09.006.

VIOLA P and JONES M. Rapid object detection using a boosted cascade of simple features[C]. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, Hawaii, 2001, (I): 511-518.

ZHANG Q and LEVINE M D. Robust multi-focus image fusion using multi-task sparse representation and spatial context[J]. IEEE Transactions on Image Processing, 2016, 25(5): 2045-2058. doi: 10.1109/TIP.2016.2524212.

ZHANG B, LU X, PEI H, et al. Multi-focus image fusion algorithm based on focused region extraction[J]. Neurocomputing, 2016, 174(PB): 733-748. doi: 10.1016/j.ins. 2016.02.030.

LIU Y, CHEN X, PENG H, et al. Multi-focus image fusion with a deep convolutional neural network[J]. Information Fusion, 2017, 36: 191-207. doi: 10.1016/j.inffus.2016.12.001.

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