Digital refocusing based on deep learning in optical coherence tomography
Zhuoqun Yuan, Di Yang, Zihan Yang, Jingzhu Zhao, Yanmei Liang
Abstract
We present a deep learning-based digital refocusing approach to extend depth of focus for optical coherence tomography (OCT) in this paper. We built pixel-level registered pairs of en face low-resolution (LR) and high-resolution (HR) OCT images based on experimental data and introduced the receptive field block into the generative adversarial networks to learn the complex mapping relationship between LR-HR image pairs. It was demonstrated by results of phantom and biological samples that the lateral resolutions of OCT images were improved in a large imaging depth clearly. We firmly believe deep learning methods have broad prospects in optimizing OCT imaging.
Topics & Concepts
Optical coherence tomographyDeep learningArtificial intelligenceComputer scienceComputer visionFocus (optics)Imaging phantomCoherence (philosophical gambling strategy)PixelGenerative adversarial networkOpticsPattern recognition (psychology)PhysicsQuantum mechanicsOptical Coherence Tomography ApplicationsImage Processing Techniques and ApplicationsCell Image Analysis Techniques