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

2026-05-24

AI Summary

This work addresses the limitations of conventional light-field microscopy for three-dimensional reconstruction—namely, low spatial resolution, severe artifacts, and high computational cost—as well as the insufficient accuracy and generalization of existing learning-based approaches. The authors propose a Three-step Conditional Diffusion (TCD) method that reformulates the diffusion model into a deterministic three-step sampling pipeline, integrated with a lightweight conditional U-Net to enable efficient, high-fidelity reconstruction. Notably, they introduce an Inter-Class Detection (ICD) module, the first of its kind, to enhance robustness against anomalous inputs. Evaluated through cross-dataset training and testing, the proposed method significantly outperforms current state-of-the-art techniques in reconstruction quality, generalization capability, and inference speed, offering a practical and efficient solution for 3D imaging in light-field microscopy.

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.

Problem

• light-field microscopy
• 3D reconstruction
• diffusion models
• reconstruction accuracy
• generalization capability

Innovation

• Three-Step Diffusion
• Light-Field Microscopy
• Conditional U-Net
• Inter-Class Detection
• 3D Reconstruction