Title: Implicit Reconstruct Spatiotemporal Super-Resolution Microscopy in Arbitrary Dimension
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Biological Imaging, Fluorescence Microscopy, Implicit Neural Representation, Arbitrary Scale Super-Resolution, 4D Medical Imaging

Abstract: High-resolution 4D fluorescence microscopy imaging, essential for deciphering dynamic biological processes, is typically challenged by insufficient spatiotemporal resolutions. Phototoxicity, photobleaching, and the anisotropic resolution inherent in current live cell image acquisition systems. To address these challenges, we propose an implicit neural representation-based arbitrary scale super-resolution framework, termed SpatimeINR, which leverages spatiotemporal latent representation in conjunction with a multilayer perceptron for 4D rendering, while incorporating cycle-consistency loss to ensure fidelity with the original data. Extensive experiments on lung cancer cell and C.elegans cell-membrane fluorescence datasets demonstrate that our approach significantly outperforms state-of-the-art methods in both temporal and spatial super-resolution tasks. Ablation studies further confirmed the critical contributions of the spatiotemporal latent representation, 4D rendering, and cycle-consistency loss.

TL;DR: We introduce SpatimeINR, an implicit neural representation framework that achieves arbitrary scale spatiotemporal super-resolution for 4D imaging data while effectively recovering fine spatiotemporal details in complex dynamic scenarios.