3 papers
eess.IV2025
Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging
Canberk Ekmekci, Mujdat Cetin
Uncertainty quantification plays an important role in achieving trustworthy and reliable learning-based computational imaging. Recent advances in generative modeling and Bayesian n…
eess.IV2024
Integrating Generative and Physics-Based Models for Ptychographic Imaging with Uncertainty Quantification
Canberk Ekmekci, Tekin Bicer, Zichao Wendy Di +2
Ptychography is a scanning coherent diffractive imaging technique that enables imaging nanometer-scale features in extended samples. One main challenge is that widely used iterativ…
eess.IV2022
Uncertainty Quantification for Deep Unrolling-Based Computational Imaging
Canberk Ekmekci, Mujdat Cetin
Deep unrolling is an emerging deep learning-based image reconstruction methodology that bridges the gap between model-based and purely deep learning-based image reconstruction meth…