反褶积
计算机科学
人工智能
深度学习
卷积神经网络
算法
领域(数学)
人工神经网络
可微函数
模式识别(心理学)
数学
数学分析
纯数学
作者
Kyrollos Yanny,Kristina Monakhova,Richard Shuai,Laura Waller
出处
期刊:Optica
[The Optical Society]
日期:2022-01-12
卷期号:9 (1): 96-96
被引量:30
标识
DOI:10.1364/optica.442438
摘要
Deconvolution can be used to obtain sharp images or volumes from blurry or encoded measurements in imaging systems. Given knowledge of the system’s point spread function (PSF) over the field of view, a reconstruction algorithm can be used to recover a clear image or volume. Most deconvolution algorithms assume shift-invariance; however, in realistic systems, the PSF varies laterally and axially across the field of view due to aberrations or design. Shift-varying models can be used, but are often slow and computationally intensive. In this work, we propose a deep-learning-based approach that leverages knowledge about the system’s spatially varying PSFs for fast 2D and 3D reconstructions. Our approach, termed MultiWienerNet, uses multiple differentiable Wiener filters paired with a convolutional neural network to incorporate spatial variance. Trained using simulated data and tested on experimental data, our approach offers a 625 − 1600 × increase in speed compared to iterative methods with a spatially varying model, and outperforms existing deep-learning-based methods that assume shift invariance.
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