Opt Lett. 2023 Feb 15;48(4):940-943. doi: 10.1364/OL.478885.
Fluorescence microscopy typically suffers from aberration induced by system and sample, which could be circumvented by image deconvolution. We proposed a novel, to the best of our knowledge, Richardson-Lucy (RL) model-driven deconvolution framework to improve reconstruction performance and speed. Two kinds of neural networks within this framework were devised, which are partially interpretable compared with previous deep learning methods. We first introduce RL into deep feature space, which has superior generalizability to the convolutional neural networks (CNN). We further accelerate it with an unmatched backprojector, providing a five times faster reconstruction speed than classic RL. Our deconvolution approaches outperform both CNN and traditional methods regarding image quality for blurred images caused by out-of-focus or imaging system aberration.
荧光显微镜通常会受到系统和样本引起的像差的影响，可以通过图像反卷积来避免。我们提出了一种新颖的、据我们所知的 Richardson-Lucy (RL) 模型驱动的反卷积框架，以提高重建性能和速度。该框架内设计了两种神经网络，与以前的深度学习方法相比，它们具有部分可解释性。我们首先将 RL 引入到深度特征空间中，与卷积神经网络 (CNN) 相比，它具有更好的泛化能力。我们进一步使用无与伦比的反向投影器来加速它，比经典 RL 快五倍的重建速度。我们的反卷积方法在对离焦或成像系统像差引起的模糊图像的图像质量方面优于 CNN 和传统方法。
