Parameter-Efficient CT Reconstruction via Deep Graph Laplacian Regularization
Low-dose computed tomography (LDCT) reconstruction faces a critical tradeoff between reconstruction quality and resource requirements. While recent deep learning methods achieve state-of-the-art performance, they typically rely on over 500,000 parameters trained on large-scale datasets exceeding 35,000 scans. This work investigates whether graph-based regularization can provide meaningful noise reduction under strict resource constraints. We propose Deep Graph Laplacian Regularization (Deep GLR), integrating quadratic graph regularization into a Proximal Forward-Backward Splitting optimization framework with three lightweight CNN modules. Evaluated on the LoDoPaB-CT benchmark, Deep GLR ac...
| Authors | Veera Varuni Radhakrishnan, Chinthaka Dinesh, Qurat-ul-Ain Azim |
|---|---|
| Primary category | eess.IV |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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