We propose measurement score-based MRI reconstruction algorithms that do not require both ground-truth MRI images and precalibrated coil sensitivity maps.
@misc{Liu2025measurementscoremri,title={Measurement Score-Based MRI Reconstruction with Automatic Coil Sensitivity Estimation},author={Liu, Tingjun and Park, Chicago Y. and Hu, Yuyang and An, Hongyu and Kamilov, Ulugbek S.},year={2025},}
We propose the first measurement score-based diffusion model that directly learns partial measurement scores using only noisy and subsampled measurements, enabling modeling of the full measurement distribution and solving inverse problems.
@misc{Park2025measurementdiffusion,title={Measurement Score-Based Diffusion Model},author={Park, Chicago Y. and Shoushtari, Shirin and An, Hongyu and Kamilov, Ulugbek S.},year={2025},}
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.
@article{Park2025randomwalks,title={Random Walks With Tweedie: A Unified View of Score-Based Diffusion Models [In the Spotlight]},author={Park, Chicago Y. and McCann, Michael T. and Garcia-Cardona, Cristina and Wohlberg, Brendt and Kamilov, Ulugbek S.},journal={IEEE Signal Processing Magazine},volume={42},number={3},pages={40--51},year={2025},publisher={IEEE},doi={10.1109/MSP.2025.3590608},url={https://doi.org/10.1109/MSP.2025.3590608}}
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.
@inproceedings{Park2025pnpscore,title={Plug-and-Play Priors as a Score-Based Method},author={Park, Chicago Y. and Hu, Yuyang and McCann, Michael T. and Garcia-Cardona, Cristina and Wohlberg, Brendt and Kamilov, Ulugbek S.},booktitle={IEEE International Conference on Image Processing (ICIP)},year={2025},}
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 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},}