InverseNet: Solving Inverse Problems with Splitting Networks

Understand

We propose a new method that uses deep learning techniques to solve the inverse problems.

• The inverse problem is cast in the form of learning an end-to-end mapping from observed data to the ground-truth.
• Inspired by the splitting strategy widely used in regularized iterative algorithm to tackle inverse problems, the mapping is decomposed into two networks, with one handling the inversion of the physical forward model associated with the data term and one handling the denoising of the output from the former network, i.e., the inverted version, associated with the prior/regularization term.
• The two networks are trained jointly to learn the end-to-end mapping, getting rid of a two-step training.