| Abstract: |
| In this talk, we introduce a novel method called the learned spherical alternating direction method of multipliers (LSADMM). This method is designed to effectively remove Rician noise under spherical constraints. LSADMM unfolds the iterations of a proximal linearized ADMM solver into a deep neural network. The solver is originally used for a sphere-constrained variational model. The network alternates between lightweight, learnable gradient-descent modules and fixed, physics-based operators. Extensive numerical experiments are conducted on both synthetic and real-world datasets. The results show that LSADMM achieves competitive restoration performance. Its architecture is lightweight and requires substantially fewer parameters than conventional end-to-end deep learning methods. |
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