KEYWORDS: Optical coherence tomography, Resolution enhancement technologies, Denoising, Signal processing, Systems modeling, In vivo imaging, Image processing, Gallium nitride, Fourier transforms, Signal to noise ratio
We proposed a dual-GAN-based deep learning to enhance resolution and reduce noise of optical coherence tomography (OCT). The dual GAN was designed with a model that enhances axial resolution and a model that enhances lateral resolution and reduces noise. We demonstrated improvements on the swine coronary artery data used for training, and further validated the performance on other sample data acquired in other systems. Through this, not only the performance but also the feasibility of independent application to a specific system or sample was verified. The current approach will be highly helpful in overcoming existing limitations of OCT.
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