… where d is PSF (in general a directional first-derivative-of-Gaussian kernel, but simple
difference without Gaussian is used for simplification), g is acquired image and f is original image. …

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform
conventional image processing pipelines. Despite the enthusiasm and innovations fuelled …

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, …

… (f) Result of our N2V trained network. Both networks are not able to preserve the grainy
structure of the image, but the N2V trained network loses more high-frequency detail. …

… The final CT value for a particular video is computed as the F 1 score of completely … The
final BC(i) value for a particular video is computed as the F 1 score of correctly reconstructed …

Multiple approaches to use deep neural networks for image restoration have recently been
proposed. Training such networks requires well registered pairs of high and low-quality …

We present Labkit, a user-friendly Fiji plugin for the segmentation of microscopy image data.
It offers easy to use manual and automated image segmentation routines that can be rapidly …

To the Editor: Current state-of-the-art light-sheet microscopes rely on sophisticated control
software to perform the acquisition of gigabytes of image data per second over the course of …

… FCN based image denoising in fact implements f(x) by producing independent predictions
s ^ i = g ( x RF ( i ) ; θ ) ≈ s i for each pixel i, depending only on x RF(i) instead of on the entire …

… We then quantified the cross correlation between changes in F-actin intensity and cell height,
and found a negative peak for a time lag around 22 s, indicating that an increase in lateral F…