Cross-system biological image quality enhancement based on the
  generative adversarial network as a foundation for establishing a
  multi-institute microscopy cooperative network
Dominik Panek Carina Rząca Maksymilian Szczypior Joanna Sorysz Krzysztof Misztal Zbigniew Baster Zenon Rajfur
High-quality fluorescence imaging of biological systems is limited by
processes like photobleaching and phototoxicity, and also in many cases, by
limited access to the latest generations of microscopes. Moreover, low temporal
resolution can lead to a motion blur effect in living systems. Our work
presents a deep learning (DL) generative-adversarial approach to the problem of
obtaining high-quality (HQ) images based on their low-quality (LQ) equivalents.
We propose a generative-adversarial network (GAN) for contrast transfer between
two different separate microscopy systems: a confocal microscope (producing HQ
images) and a wide-field fluorescence microscope (producing LQ images). Our
model proves that such transfer is possible, allowing us to receive
HQ-generated images characterized by low mean squared error (MSE) values, high
structural similarity index (SSIM), and high peak signal-to-noise ratio (PSNR)
values. For our best model in the case of comparing HQ-generated images and
HQ-ground truth images, the median values of the metrics are 6x10-4, 0.9413,
and 31.87, for MSE, SSIM, and PSNR, respectively. In contrast, in the case of
comparison between LQ and HQ ground truth median values of the metrics are
equal to 0.0071, 0.8304, and 21.48 for MSE, SSIM, and PSNR respectively.
Therefore, we observe a significant increase ranging from 14% to 49% for SSIM
and PSNR respectively. These results, together with other single-system
cross-modality studies, provide proof of concept for further implementation of
a cross-system biological image quality enhancement.