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7 March 2022 Image enhancement for optical coherence tomography with super-resolution generative adversarial networks (SRGAN)
Timothy T. Yu, Da Ma, Jayden Cole, Myeong Jin Ju, Mirza Faisal Beg, Marinko V. Sarunic
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Proceedings Volume PC11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI; PC119481F (2022) https://doi.org/10.1117/12.2610360
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
We present a novel approach of leveraging deep learning to reconstruct high-resolution OCT B-scans from reduced axial resolution data. In this work, the original OCT signal is used as the ground truth, and lower resolution was simulated by windowing the interference fringes. A super-resolution pixel-to-pixel generative adversarial network (GAN) was investigated for reconstructing high-resolution OCT data in the spatial domain and is compared against reconstructing in the spectral domain.
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Timothy T. Yu, Da Ma, Jayden Cole, Myeong Jin Ju, Mirza Faisal Beg, and Marinko V. Sarunic "Image enhancement for optical coherence tomography with super-resolution generative adversarial networks (SRGAN)", Proc. SPIE PC11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, PC119481F (7 March 2022); https://doi.org/10.1117/12.2610360
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KEYWORDS
Optical coherence tomography
Super resolution
Image enhancement
Gallium nitride
Spectral resolution
Image resolution
Resolution enhancement technologies
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