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Microscopy Image Restoration using Deep Learning on W2S

2020-04-22 22:14:19
Martin Chatton
arXiv_CV
             
Abstract

We leverage deep learning techniques to jointly denoise and super-resolve biomedical images acquired with fluorescence microscopy. We develop a deep learning algorithm based on the networks and method described in the recent W2S paper to solve a joint denoising and super-resolution problem. Specifically, we address the restoration of SIM images from widefield images. Our TensorFlow model is trained on the W2S dataset of cell images and is made accessible online in this repository: this https URL. On test images, the model shows a visually-convincing denoising and increases the resolution by a factor of two compared to the input image. For a 512 $\times$ 512 image, the inference takes less than 1 second on a Titan X GPU and about 15 seconds on a common CPU. We further present the results of different variations of losses used in training.

Abstract (translated)

URL

https://arxiv.org/abs/2004.10884

PDF

https://arxiv.org/pdf/2004.10884.pdf

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