Deep Inertia $L_p$ Half-Quadratic Splitting Unrolling Network for Sparse View CT Reconstruction

• URL: http://arxiv.org/abs/2408.06600v1
• Date: Tue, 13 Aug 2024 03:32:59 GMT
• Title: Deep Inertia $L_p$ Half-Quadratic Splitting Unrolling Network for Sparse View CT Reconstruction
• Authors: Yu Guo, Caiying Wu, Yaxin Li, Qiyu Jin, Tieyong Zeng,
• Abstract summary: Sparse view computed tomography (CT) reconstruction poses a challenging ill-posed inverse problem, necessitating effective regularization techniques. We employ $L_p$-norm regularization to induce sparsity and introduce inertial steps, leading to the development of the inertial $L_p$-norm half-quadratic splitting algorithm. Our proposed algorithm surpasses existing methods, particularly excelling in fewer scanned views and complex noise conditions.
• Score: 20.632166806596278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse view computed tomography (CT) reconstruction poses a challenging ill-posed inverse problem, necessitating effective regularization techniques. In this letter, we employ $L_p$-norm ($0<p<1$) regularization to induce sparsity and introduce inertial steps, leading to the development of the inertial $L_p$-norm half-quadratic splitting algorithm. We rigorously prove the convergence of this algorithm. Furthermore, we leverage deep learning to initialize the conjugate gradient method, resulting in a deep unrolling network with theoretical guarantees. Our extensive numerical experiments demonstrate that our proposed algorithm surpasses existing methods, particularly excelling in fewer scanned views and complex noise conditions.

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