Multi-focus Image Fusion with Multi-scale Based on Fractional Order Differentiation

被引：0
|
作者：
Zhang X.-F. [1 ]
He H. [1 ]
机构：
[1] School of Sciences, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2021年 / 42卷 / 08期
关键词：
Fractional order differentiation; Image fusion; Multi-focused image; Multi-scale approach;
D O I：
10.12068/j.issn.1005-3026.2021.08.002
中图分类号：
学科分类号：
摘要：
Aiming at multi-focus image fusion, the complementary information at each scale is fused in a targeted manner using a multi-scale fusion method. First, the source image is decomposed into a background layer and a detail layer using the L0 gradient minimization smoothing method, and then a fractional-order edge detection mask is used to preserve the edge information in the background layer, and a fractional-order gradient energy operator is introduced in the detail layer for weight assignment. The experimental results show that the proposed fractional-order gradient energy operator is more sensitive than the integer-order gradient energy clarity operator, which is consistent with the human visual perception. The fusion method proposed effectively avoids artifacts and block effects in the fused images, and retains the source image information more fully. © 2021, Editorial Department of Journal of Northeastern University. All right reserved.
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收藏
页码：1071 / 1078
页数：7
相关论文
共 20 条
  • [1] Yin M, Liu X, Liu Y, Et al., Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled shearlet transform domain [J], IEEE Transactions on Instrumentation and Measurement, 68, 1, pp. 49-64, (2018)
  • [2] Petrovic V S, Xydeas C S., Gradient-based multiresolution image fusion [J], IEEE Transactions on Image Processing, 13, 2, pp. 228-237, (2004)
  • [3] Pu T, Ni G G., Contrast-based image fusion using the discrete wavelet transform [J], Optical Engineering, 39, 8, pp. 2075-2082, (2000)
  • [4] Zhang Q, Li G H, Cao Y F, Et al., Multi-focus image fusion based on non-negative sparse representation and patch-level consistency rectification [J], Pattern Recognition, 104, (2020)
  • [5] Sujatha K, Punithavathani D S., Optimized ensemble decision-based multi-focus image fusion using binary genetic Grey-Wolf optimizer in camera sensor networks [J], Multimedia Tools & Applications, 77, pp. 1735-1759, (2018)
  • [6] Duan J, Chen L, Chen C L P., Multifocus image fusion with enhanced linear spectral clustering and fast depth map estimation [J], Neurocomputing, 318, pp. 43-54, (2018)
  • [7] Bouzos O, Andreadis I, Mitianoudis N., Conditional random field model for robust multi-focus image fusion [J], IEEE Transactions on Image Processing, 28, 11, pp. 5636-5648, (2019)
  • [8] Li S T, Kang X D, Hu J W., Image fusion with guided filtering [J], IEEE Transactions on Image Processing, 22, 7, pp. 2864-2875, (2013)
  • [9] Wang Bin, Pu Yi-fei, Zhou Ji-liu, New fractional order differential based image edge detection operator [J], Application Research of Computers, 29, 8, pp. 3160-3162, (2012)
  • [10] Zhang Xue-feng, Yan Hui, Image denoising and enhancement algorithm based on the median filtering and the fractional order filtering [J], Journal of Northeastern University(Nature Science), 41, 4, pp. 482-487, (2020)
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