Model-based deep learning approaches to the Helsinki Tomography Challenge 2022

Abstract

The Finnish Inverse Problems Society organized the Helsinki Tomography Challenge (HTC) in 2022 to reconstruct an image with limited-angle measurements. We participated in this challenge and developed two methods: an Edge Inpainting method and a Learned Primal-Dual (LPD) network. The Edge Inpainting method involves multiple stages, including classical reconstruction using Perona-Malik, detection of visible edges, inpainting invisible edges using a U-Net, and final segmentation using a U-Net. The LPD approach adapts the classical LPD by using large U-Nets in the primal update and replacing the adjoint with the filtered back projection (FBP). Since the challenge only provided five samples, we generated synthetic data to train the networks. The Edge Inpainting Method performed well for viewing ranges above 70 degrees, while the LPD approach performed well across all viewing ranges and ranked second overall in the challenge.