MicroDiffuse3D: A Foundation Model for 3D Microscopy Imaging Restoration

TL;DR

MicroDiffuse3D is a pretrained foundation model that significantly improves 3D microscopy image restoration, enabling high-resolution, high-throughput volumetric imaging in chemical microscopy.

Contribution

The paper introduces MicroDiffuse3D, a novel pretrained model for 3D microscopy restoration, demonstrating its effectiveness across multiple challenging scenarios.

Findings

Achieved 10.58% improvement in segmentation quality.

Produced clearer depth continuity with fewer artifacts.

Delivered significant gains in low SNR denoising and super-resolution tasks.

Abstract

Chemical imaging enables label-free visualization of cells, tissues and living systems while providing direct biochemical information that is difficult to obtain with conventional fluorescence microscopy. Despite its promise in applications ranging from intraoperative diagnosis to drug-response analysis, its broader use remains limited by slow data acquisition, particularly for three-dimensional imaging. Here we present MicroDiffuse3D, a pretrained foundation model for 3D microscopy image restoration that recovers high-quality volumetric structure from degraded low-resolution measurements acquired at substantially higher throughput. We evaluated MicroDiffuse3D across three challenging restoration settings, including 3D super-resolution under 16-fold volumetric sparsity, joint degradation in resolution and noise, and 3D denoising in the low signal-to-noise ratio (SNR) regime, where the model delivered clear gains over strong baselines. Under the sparse 3D super-resolution setting, MicroDiffuse3D produced clearer continuity across depth with fewer artifacts and improved segmentation quality by 10.58% and line-profile concordance by 15.59%. Together, our results establish pretrained 3D restoration as a broadly applicable strategy for overcoming the throughput and SNR limitations in volumetric chemical imaging, enabling high-resolution analysis at scales and speeds that were previously difficult to achieve.