Nonconvex ${{L_ {{1/2}}}} $-Regularized Nonlocal Self-similarity Denoiser for Compressive Sensing based CT Reconstruction

• URL: http://arxiv.org/abs/2205.07185v1
• Date: Sun, 15 May 2022 05:24:48 GMT
• Title: Nonconvex ${{L_ {{1/2}}}} $-Regularized Nonlocal Self-similarity Denoiser for Compressive Sensing based CT Reconstruction
• Authors: Yunyi Li (1), Yiqiu Jiang (2), Hengmin Zhang (3), Jianxun Liu (1), Xiangling Ding (1) and Guan Gui (4) ((1) School of Computer Science and Engineering, Hunan University of Science and Technology (2) Department of Sports Medicine and Joint Surgery, Nanjing First Hospital, Nanjing Medical University (3) Department of Computer and Information Science, University of Macau (4) College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications)
• Abstract summary: Recently, the nonL1/2 $-norm has achieved promising performance in recovery, while the applications are unsatisfactory due to its nonsimilarity. In this paper, we develop a CT reconstruction problem which is sparse on $_ $ $ minimization. Extensive results on typical images have demonstrated our approach to achieve better performance.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Compressive sensing (CS) based computed tomography (CT) image reconstruction aims at reducing the radiation risk through sparse-view projection data. It is usually challenging to achieve satisfying image quality from incomplete projections. Recently, the nonconvex ${{L_ {{1/2}}}} $-norm has achieved promising performance in sparse recovery, while the applications on imaging are unsatisfactory due to its nonconvexity. In this paper, we develop a ${{L_ {{1/2}}}} $-regularized nonlocal self-similarity (NSS) denoiser for CT reconstruction problem, which integrates low-rank approximation with group sparse coding (GSC) framework. Concretely, we first split the CT reconstruction problem into two subproblems, and then improve the CT image quality furtherly using our ${{L_ {{1/2}}}} $-regularized NSS denoiser. Instead of optimizing the nonconvex problem under the perspective of GSC, we particularly reconstruct CT image via low-rank minimization based on two simple yet essential schemes, which build the equivalent relationship between GSC based denoiser and low-rank minimization. Furtherly, the weighted singular value thresholding (WSVT) operator is utilized to optimize the resulting nonconvex ${{L_ {{1/2}}}} $ minimization problem. Following this, our proposed denoiser is integrated with the CT reconstruction problem by alternating direction method of multipliers (ADMM) framework. Extensive experimental results on typical clinical CT images have demonstrated that our approach can further achieve better performance than popular approaches.

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