# Off-the-grid model based deep learning (O-MODL)

## TL;DR

This paper presents an off-the-grid deep learning approach for image reconstruction that learns non-linear Fourier space relations, offering reduced computational complexity and promising preliminary results compared to existing methods.

## Contribution

It introduces a novel off-the-grid deep learning model that learns non-linear annihilation relations in Fourier space, differing from current approaches.

## Key findings

- Preliminary comparisons show potential advantages over image domain MoDL.
- Significant reduction in computational complexity.
- Effective learning of non-linear Fourier space relations.

## Abstract

We introduce a model based off-the-grid image reconstruction algorithm using deep learned priors. The main difference of the proposed scheme with current deep learning strategies is the learning of non-linear annihilation relations in Fourier space. We rely on a model based framework, which allows us to use a significantly smaller deep network, compared to direct approaches that also learn how to invert the forward model. Preliminary comparisons against image domain MoDL approach demonstrates the potential of the off-the-grid formulation. The main benefit of the proposed scheme compared to structured low-rank methods is the quite significant reduction in computational complexity.