CMP events

Dr. Florian Jug presents Content-Aware Image Restoration for Light and Electron Microscopy

On 2019-06-26 11:00 at G205, Karlovo náměstí 13, Praha 2
In recent years, fluorescent light microscopy and cryo-electron microscopy saw
tremendous technological advances. Using light microscopes, we routinely image
beyond the resolution limit, acquire large volumes at high temporal resolution,
and capture many hours of video material showing processes of interest inside
cells, in tissues, and in developing organisms. Cryo-electron microscopes, at
the same time, are capable of visualizing cellular building-blocks in their
native environment at close to atomic resolution. Despite these possibilities,
the analysis of raw images is usually non-trivial, error-prone, and cumbersome.

Here we show how machine learning, ie. neural networks, can help to tap the
full potential of raw microscopy data by applying content-aware image
restoration (CARE) techniques. Several examples in the context of light
microscopy (LM) and cryo-electron microscopy (EM) illustrate how downstream
analysis pipelines lead to improved (automated) results when applied to
content-aware restorations.

While our recently published results on LM data [1] do profit from the fact
that single high-quality, low-noise acquisitions can directly be recorded, in
other occasions, this is not possible (e.g. for cryo-EM). Hence, we developed
CARE variations [2,3,4] that do not require the acquisition of high-quality
examples but can be trained from noisy images alone.

[1] M Weigert, U Schmidt, et al.; Content-aware image restoration: pushing the
limits of fluorescence microscopy; bioRxiv, 236463
[2] TO Buchholz, M Jordan, G Pigino, F Jug; Cryo-CARE: Content-Aware Image
Restoration for Cryo-Transmission Electron Microscopy Data; ISBI'19; preprint:
[3] A Krull, TO Buchholz, F Jug; Noise2Void-Learning Denoising from Single
Noisy Images; CVPR'19, preprint: arXiv:1811.10980
[4] A Krull, T Vicar, F Jug; Probabilistic Noise2Void: Unsupervised
Content-Aware Denoising; arXiv arXiv:1906.00651 [eess.IV]