16 citations · 74 across the 31 of their papers we have counts for
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Learning convex regularizers satisfying the variational source condition for inverse problems
Subhadip Mukherjee, Carola-Bibiane Schönlieb, Martin Burger
Variational regularization has remained one of the most successful approaches for reconstruction in imaging inverse problems for several decades. With the emergence and astonishing…
StyleGAN-induced data-driven regularization for inverse problems
Arthur Conmy, Subhadip Mukherjee, Carola-Bibiane Schönlieb
Recent advances in generative adversarial networks (GANs) have opened up the possibility of generating high-resolution photo-realistic images that were impossible to produce previo…
Stochastic Primal-Dual Deep Unrolling
Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb
We propose a new type of efficient deep-unrolling networks for solving imaging inverse problems. Conventional deep-unrolling methods require full forward operator and its adjoint a…
End-to-end reconstruction meets data-driven regularization for inverse problems
Subhadip Mukherjee, Marcello Carioni, Ozan Öktem +1
We propose an unsupervised approach for learning end-to-end reconstruction operators for ill-posed inverse problems. The proposed method combines the classical variational framewor…
Adversarially learned iterative reconstruction for imaging inverse problems
Subhadip Mukherjee, Ozan Öktem, Carola-Bibiane Schönlieb
In numerous practical applications, especially in medical image reconstruction, it is often infeasible to obtain a large ensemble of ground-truth/measurement pairs for supervised l…