We propose the new approach combining network pruning and fine-tuning to enhance the efficiency of model-based deep learning for imaging inverse problems.
@article{Park2023pruning,title={Efficient Model-based Deep Learning via Network Pruning and Fine-Tuning},author={Park, Chicago Y. and Gan, Weijie and Zou, Zihao and Hu, Yuyang and Sun, Zhixin and Kamilov, Ulugbek S.},journal={Journal of Mathematical Imaging and Vision},year={2025},}
We reinterpret plug-and-play (PnP) methods as score-based approaches, enabling the use of score-based diffusion model (SBM) priors in PnP without retraining, allowing direct comparison with SBM-based reconstruction methods.
We present a configurable theoretical framework for score-based diffusion models, leading to generic algorithm templates of influential diffusion models and enabling faster and more flexible sampling strategies.
We propose the theoretical and experimental evidence for the effective use of expansive CNN denoisers in the PnP-ADMM framework to solve convex or non-convex imaging inverse problems.
@inproceedings{Park2023pnpadmm,author={Park, Chicago. Y. and Shoushtari, S. and Gan, W. and Kamilov, Ulugbek S.},booktitle={IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},title={Convergence of Nonconvex PnP-ADMM with MMSE Denoisers},year={2023},address={Los Suenos, Costa Rica},pages={511--515},}
