Super-Resolution Benchmarking for 3D Image-to-Image Fusion Problem

Fluorescence microscopy faces challenges in resolution, phototoxicity, and anisotropic artifacts. The Fuse My Cells challenge, organized by France-BioImaging, aims to develop deep learning models that predict fused 3D volumes from single-view acquisitions, reducing phototoxic exposure while enhancing resolution. In this work, we benchmark state-of-the-art super-resolution models, including DFCAN, RCAN-3D, UNETR, and a 3D-adapted RCAN-it, evaluating their performance on the Fuse My Cells challenge dataset, which encompasses 802 3D light-sheet microscopy images. A novel training strategy prioritizing high-discrepancy regions optimizes efficiency and improves reconstruction accuracy. Our findings suggest that super-resolution models can not fully reconstruct the information on those image areas with minimum signal information. Code and documentation can be found at https://github.com/danifranco/BiaPy.