High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning

• Published in: arXiv.org
• Main Authors: Qiangjiang Hao, Zhou, Kang, Yang, Jianlong, Fang, Liyang, Chai, Zhengjie, Ma, Yuhui, Hu, Yan, Gao, Shenghua, Liu, Jiang
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 11.02.2020
• Subjects: Deep learning; Degeneration; Image acquisition; Image detection; Image reconstruction; Interference fringes; Machine learning; Medical imaging; Noise reduction; Optical Coherence Tomography; Physics - Optics; Pixels; Signal to noise ratio; System effectiveness; Telemedicine; Tomography; Transmission efficiency

Reducing the bit-depth is an effective approach to lower the cost of optical coherence tomography (OCT) systems and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit-depth will lead to the degeneration of the detection sensitivity thus reduce the signal-to...