Deep learning approaches for unwrapping phase images with steep spatial gradients: a simulation

We explore different deep learning-based approaches to solve the problem of phase unwrapping in objects with high spatial gradients, which is applicable to many fields in medicine, biology and remote sensing. We simulate data with high spatial gradients to compare the quality of the solution and the runtime obtained when addressing this problem either as an inverse problem or as a semantic segmentation problem.