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5 March 2021 Deep learning-based denoising in spectrally-encoded confocal microscopy
Jingwei Zhao, Manu Jain, Ucalene Harris, Kivanc Kose, Clara Curiel-Lewandrowski, Dongkyun Kang
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Proceedings Volume 11620, Endoscopic Microscopy XVI; 1162004 (2021) https://doi.org/10.1117/12.2577321
Event: SPIE BiOS, 2021, Online Only
Abstract
In this paper, we demonstrate deep learning-based denoising of high-speed (180 fps) confocal images obtained with our low-cost SECM device. The CARE network was trained with 3090 high- and low-SNR image pairs on the Google Colab platform and tested with 45 unseen image pairs. The CARE prediction showed significant increase of SSIM and PSNR, and reduction of the banding noise while maintaining the cellular details. The preliminary results show the potential of using a deep learning-based denoising approach to enable high-speed SECM imaging.
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Jingwei Zhao, Manu Jain, Ucalene Harris, Kivanc Kose, Clara Curiel-Lewandrowski, and Dongkyun Kang "Deep learning-based denoising in spectrally-encoded confocal microscopy", Proc. SPIE 11620, Endoscopic Microscopy XVI, 1162004 (5 March 2021); https://doi.org/10.1117/12.2577321
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KEYWORDS
Confocal microscopy
Denoising
Signal to noise ratio
CMOS sensors
Skin
Image restoration
Interference (communication)
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