Deconvolution for multimode fiber imaging: modeling of spatially variant PSF
Deconvolution for multimode fiber imaging: modeling of spatially variant PSF
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DOI：
10.1364/boe.399983
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发表时间：
2020-07
影响因子：
3.4
通讯作者：
Raphaël Turcotte;Eusebiu Sutu;Carla C. Schmidt;N. Emptage;M. Booth
中科院分区：
文献类型：
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作者：
Raphaël Turcotte;Eusebiu Sutu;Carla C. Schmidt;N. Emptage;M. Booth
使用波前控制通过阶跃折射率多模光纤（MMF）聚焦光使得能够对生物组织进行微创内窥镜检查。这种成像系统的点扩散函数（PSF）是空间变化的，并且这种变化限制了使用大多数去卷积算法对模糊的补偿，因为它们需要均匀的PSF。然而，将空间变化的PSF建模为一系列空间不变的PSF重新打开了去卷积的可能性。为了实现这一点，我们开发了svmPSF：一个与ImageJ兼容的基于Java的开源框架。该方法在整个视场（FOV）上进行一系列点响应测量，并将主成分分析应用于测量的协方差矩阵以生成PSF模型。通过将svmPSF输出与修改后的Richardson-Lucy反卷积算法相结合，我们能够对用MMF获得的珠子和活神经元的荧光图像进行去模糊和正则化，从而有效地增加FOV。
Focusing light through a step-index multimode optical fiber (MMF) using wavefront control enables minimally-invasive endoscopy of biological tissue. The point spread function (PSF) of such an imaging system is spatially variant, and this variation limits compensation for blurring using most deconvolution algorithms as they require a uniform PSF. However, modeling the spatially variant PSF into a series of spatially invariant PSFs re-opens the possibility of deconvolution. To achieve this we developed svmPSF: an open-source Java-based framework compatible with ImageJ. The approach takes a series of point response measurements across the field-of-view (FOV) and applies principal component analysis to the measurements' co-variance matrix to generate a PSF model. By combining the svmPSF output with a modified Richardson-Lucy deconvolution algorithm, we were able to deblur and regularize fluorescence images of beads and live neurons acquired with a MMF, and thus effectively increasing the FOV.