Invertible residual networks in the context of regularization theory for linear inverse problems

Understand

Learned inverse problem solvers exhibit remarkable performance in applications like image reconstruction tasks.

• These data-driven reconstruction methods often follow a two-step scheme.
• First, one trains the often neural network-based reconstruction scheme via a dataset.
• Second, one applies the scheme to new measurements to obtain reconstructions.