Three-Step Conditional Diffusion 3D Reconstruction for Light-Field Microscopy

• URL: http://arxiv.org/abs/2605.24959v1
• Date: Sun, 24 May 2026 09:16:35 GMT
• Title: Three-Step Conditional Diffusion 3D Reconstruction for Light-Field Microscopy
• Abstract summary: Light-field microscopy enables single-shot capture of multi-angular information from biological samples.<n>Traditional physics-based algorithms often suffer from limited spatial resolution, severe artifacts, and high computational costs.<n>This paper proposes a high-fidelity Three-Step Conditional Diffusion (TCD) 3D reconstruction method for LFM.
• Score: 14.462955551711671
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Light-field microscopy (LFM) enables single-shot capture of multi-angular information from biological samples, supporting real-time volumetric imaging. However, traditional physics-based algorithms often suffer from limited spatial resolution, severe artifacts, and high computational costs. Existing learning-based methods improve inference efficiency but still face limitations in reconstruction accuracy and generalization capability. To address these challenges, this paper proposes a high-fidelity Three-Step Conditional Diffusion (TCD) 3D reconstruction method for LFM. Although conventional diffusion models have achieved remarkable success in generative modeling, their slow sampling process and the inherent trade-off between quality and efficiency hinder their application in real-time 3D imaging. We redesign the diffusion process through a deterministic three-step sampling strategy coupled with a lightweight conditional U-Net, establishing a new paradigm for fast and accurate volumetric reconstruction. Furthermore, an Inter-Class Detection (ICD) module is incorporated to identify out-of-distribution or anomalous inputs during inference, thereby enhancing model stability and reliability. Extensive experiments and cross-dataset evaluations demonstrate that TCD significantly outperforms state-of-the-art methods in both reconstruction fidelity and generalization, providing an efficient and practical 3D reconstruction solution for light-field microscopy.

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