Deep Inertia $L_p$ Half-Quadratic Splitting Unrolling Network for Sparse View CT Reconstruction
<|reference_start|>Deep Inertia $L_p$ Half-Quadratic Splitting Unrolling Network for Sparse View CT Reconstruction: 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.<|reference_end|>
@article{guo2024deep,
    title={Deep Inertia $L_p$ Half-Quadratic Splitting Unrolling Network for Sparse
  View CT Reconstruction},
    author={Yu Guo, Caiying Wu, Yaxin Li, Qiyu Jin, Tieyong Zeng},
    journal={IEEE Signal Processing Letters, 2024, 31:2030-2034},
    year={2024},
    doi={10.1109/LSP.2024.3438118},
    archivePrefix={arXiv},
    eprint={2408.06600},
    primaryClass={eess.IV cs.CV}
}