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Nonlinear Filter Combined Regularization of Compressed Sensing for CT Image Reconstruction

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International Conference on Cloud Computing and Computer Networks (CCCN 2023)

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Abstract

This chapter presents a framework of nonlinear filter-based compressed sensing (CS) applied on sparse view CT image reconstruction. The conventional algorithms of quadratic-form regularization have been recognized that they fail to take the discontinuities of images into account. They may lead to over smoothed of object boundaries or fine structures in the reconstructed images. The proposed method considers sequential changes of all the local pixels in a designed window. A kind of nonlinear filter, which is median filter, bilateral filter, or non-local weighted means filter, was embedded in the CS framework, respectively. The image reconstruction problem can be treated as a cost function minimization problem. The proposed cost function consists of data fidelity term and penalty term. The penalty term, in which the nonlinear filter works, was designed and tested in ℓ0 norm, ℓ1 norm, and ℓ2 norm. Proximal splitting theory was used for constructing iterative reconstruction methods. Furthermore, we carried out calculation acceleration by constructing row-action structure. In this chapter, we applied the new method to practical medial CT images (dental, chest, and cranial images), and it showed a superior effect in image smoothing, object boundary extracting, and texture preserving. Appropriate adjustment of nonlinear filter parameters is a little complicated, and we will promote the automatic parameter setting or reducing parameter numbers in our future work.

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Acknowledgments

This work is supported by scientific research project of Tianjin Education Commission (Grants No. 2021KJ012) and Innovation & Entrepreneurship Project for College Students (Grants No.202210066008).

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Authors and Affiliations

  1. Engineering Training Center, Tianjin University of Technology and Education, Tianjin, China

    Yang Ding

  2. Tianjin Nanyang Industrial School, Tianjin, China

    Zhirong Cui

  3. College of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China

    Hanxiu Dai

  4. Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education, Tianjin, China

    Jian Dong

Authors
  1. Yang Ding
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  2. Zhirong Cui
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  3. Hanxiu Dai
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  4. Jian Dong
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Editors and Affiliations

  1. School of Software, Shangdong University, Jinan, China

    Lei Meng

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Ding, Y., Cui, Z., Dai, H., Dong, J. (2024). Nonlinear Filter Combined Regularization of Compressed Sensing for CT Image Reconstruction. In: Meng, L. (eds) International Conference on Cloud Computing and Computer Networks. CCCN 2023. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-47100-1_4

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  • DOI: https://doi.org/10.1007/978-3-031-47100-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47099-8

  • Online ISBN: 978-3-031-47100-1

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