CARE

Content-aware image restoration: pushing the limits of fluorescence microscopy.  BioRxiv, 236463.
Fluorescence microscopy is a key driver of discoveries in the life-sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample.  These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth.  In this work we show how deep learning enables biological observations beyond the physical limitations of microscopes.  On seven concrete examples we illustrate how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how isotropic resolution can be achieved even with a 10-fold under-sampling along the axial direction, and how diffraction-limited structures can be resolved at 20-times higher frame-rates compared to state-of-the-art methods.  All developed image restoration methods are freely available as open source software.

もしかしたらこれはスゴイんじゃなかろうか。コンフォーカルとかの蛍光顕微鏡画像のimage restorationを、deep learningによって可能にした CARE (content-aware restoration) というプログラム。ディープラーニングの難しいことはよう分からんけども、紹介されているサンプル画像での効果は素晴らしいように見える。Fijiで動かせるみたいだから、ちょっと使ってみましょう。
「This means that the application of CARE to biological images allows to transcend the limitations of the design-space tetrahedron, pushing the limits of the possible in fluorescence microscopy through machine learned image computation.」