DeepDenoise: a deep learning model for noise reduction in low SNR imaging conditions

Tonislav Ivanov; Ayush Kumar; Denis Sharoukhov; Francis Ortega; Matthew Putman

doi:10.1117/12.2568986

20 August 2020 DeepDenoise: a deep learning model for noise reduction in low SNR imaging conditions

Tonislav Ivanov, Ayush Kumar, Denis Sharoukhov, Francis Ortega, Matthew Putman

Proceedings Volume 11511, Applications of Machine Learning 2020; 1151107 (2020) https://doi.org/10.1117/12.2568986
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

Low signal to noise ratio (SNR) conditions degrade microscopy imaging quality, which complicates downstream post-processing and analysis. One conventional method to improve SNR by reducing noise is to average a large number of sequentially acquired images. However, this results in increased data acquisition time and reduced throughput. Longer exposures are also problematic for light-sensitive samples. We developed an alternative method, using a deep learning model based on the U-Net architecture that significantly reduces the number of images required to obtain exceptionally high SNR. Our model takes 5 noisy grayscale images as an input to generates a denoised image as an output. The model is trained on synthetically generated examples with added noise and fine tuned on real data. We demonstrate fast and robust denoising for images of fluorescent samples. Our method is capable of enhancing features while minimizing sample degradation from prolonged light exposure.

Conference Presentation

Citation Download Citation

Tonislav Ivanov, Ayush Kumar, Denis Sharoukhov, Francis Ortega, and Matthew Putman "DeepDenoise: a deep learning model for noise reduction in low SNR imaging conditions", Proc. SPIE 11511, Applications of Machine Learning 2020, 1151107 (20 August 2020); https://doi.org/10.1117/12.2568986

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