# Learning convex regularizers satisfying the variational source condition for inverse problems

@article{Mukherjee2021LearningCR, title={Learning convex regularizers satisfying the variational source condition for inverse problems}, author={Subhadip Mukherjee and Carola-Bibiane Sch{\"o}nlieb and Martin Burger}, journal={ArXiv}, year={2021}, volume={abs/2110.12520} }

Variational regularization has remained one of the most successful approaches for reconstruction in imaging inverse problems for several decades. With the emergence and astonishing success of deep learning in recent years, a considerable amount of research has gone into data-driven modeling of the regularizer in the variational setting. Our work extends a recently proposed method, referred to as adversarial convex regularization (ACR), that seeks to learn data-driven convex regularizers via…

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