通过可学习的数字滤波器，实现散射组织中大景深显微成像。
DeepFilters: Scattering-Aware Pupil Engineering with Learned Digital Filter Reconstruction for Extended Depth of Field Microscopy
- 联合优化瞳孔滤波器与数字滤波重建网络，基于可微物理模型。
- 在生物组织中实现120微米深度信号恢复，清晰介质下焦深超400微米。
- 无需重训练即可泛化到不同散射环境，适合活体深层成像研究。
扩展景深显微镜通过设计点扩散函数（PSF）将轴向信息编码至单次成像中，但传统与深度光学方法在散射组织中易退化。本文提出DeepFilters，一种散射感知的深度光学框架，通过校准的可微前向模型联合优化参数化瞳孔滤波器与基于数字滤波的重建网络，实现无需重训练的广泛泛化。结合经验散射核、物理引导正则化及混合遗传-梯度初始化策略，DeepFilters在透明介质中将PSF扩展至超过16微米，达400微米以上；在生物组织中实现超过120微米深度的信号恢复，已在固定脑切片与海胆胚胎样本中验证。
原文摘要 · Abstract (English)
Extended depth of field microscopy encodes axial information into a single acquisition through engineered point spread functions, but conventional and deep optics approaches are subject to degradation in scattering tissue. We introduce DeepFilters, a scattering-aware deep optics framework that jointly optimizes a parameterized pupil filter and a digital-filter-based reconstruction network through a calibrated differentiable forward model to achieve broad generalization without retraining. Incorporating empirical scattering kernels, physics-guided regularization, and a hybrid genetic-gradient initialization strategy, DeepFilters extends the PSF from 16 micron to >400 micron in clear media and enables signal recovery beyond 120 micron deep in biological tissues, validated across fixed brain slices and sea urchin embryos.
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