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An Efficient Medical Image Fusion via Online Convolutional Sparse Coding with Sample-Dependent Dictionary

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Image and Graphics (ICIG 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14359))

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Abstract

Convolutional sparse coding (CSC) as an interpretable signal representation and decomposition model has achieved promising performance in medical image fusion by virtue of translation-invariant dictionary. CSC-based compensates for the limited detail preservation capability and high sensitivity to misregistration of SR-based fusion methods. However, existing CSC-based fusion methods have high time consumption due to batch mode. The online convolutional sparse coding (SCSC) model of Sample-Dependent dictionary borrows the idea of separable filters with much lower time cost than CSC. In this paper, SCSC is introduced to medical image fusion to balance fusion performance and time consumption. The proposed method adopts classical ‘decomposition-fusion-reconstruction’ framework. Firstly, source images are decomposed into base and detail layers using two-scale image decomposition (Fast Fourier Transform). Secondly, average strategy is applied to base layer and detail layer uses SCSC to obtain fused detail components. Finally, two-scale image reconstruction (inverse Fast Fourier Transform) is used to reconstruct fused image. The analysis of subjective and objective results shows that our method improves efficiency while ensuring excellent fusion performance.

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Acknowledgments

This work is supported by Sichuan Science and Technology Program(2023NSFSC0495), Si-chuan University and Luzhou Municipal People’s Government Strategic cooperation pro-jects(2020CDLZ-10) and Colleague Project of Intelligent Policing Key Laboratory of Sichuan Province (ZNJW2022ZZMS001, ZNJW2023ZZQN004).

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Authors and Affiliations

  1. Intelligent Policing Key Laboratory of Sichuan Province, Sichuan Police College, 186 Longtouguan Road, Luzhou, 646000, Sichuan, China

    Chengfang Zhang, Chao Zhang & Kai Yi

  2. National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, South 1st Ring Road, Chengdu, 610065, Sichuan, China

    Chengfang Zhang

  3. College of Computer Science, Sichuan University, South 1st Ring Road, Chengdu, 610065, Sichuan, China

    Ziliang Feng

Authors
  1. Chengfang Zhang
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  2. Ziliang Feng
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  3. Chao Zhang
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  4. Kai Yi
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Corresponding author

Correspondence to Chengfang Zhang .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. University of Sydney, Sydney, NSW, Australia

    Wanli Ouyang

  3. Shenzhen University, Shenzhen, China

    Hui Huang

  4. Tsinghua University, Beijing, China

    Jiwen Lu

  5. Dalian University of Technology, Dalian, China

    Risheng Liu

  6. Institute of Automation, CAS, Beijing, China

    Jing Dong

  7. University of Technology Sydney, Sydney, NSW, Australia

    Min Xu

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Zhang, C., Feng, Z., Zhang, C., Yi, K. (2023). An Efficient Medical Image Fusion via Online Convolutional Sparse Coding with Sample-Dependent Dictionary. In: Lu, H., et al. Image and Graphics . ICIG 2023. Lecture Notes in Computer Science, vol 14359. Springer, Cham. https://doi.org/10.1007/978-3-031-46317-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-46317-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46316-7

  • Online ISBN: 978-3-031-46317-4

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