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.

通过生成对抗网络的方法，我们成功实现了从低质量图像到高质量图像的对比转换，产生了具有低均方误差、高结构相似性指数和高峰值信噪比的高质量图像，为跨系统的生物图像质量增强进一步的实施提供了概念验证。

基于生成对抗网络的跨系统生物图像质量增强：建立多机构显微镜合作网络的基础

Cross-system biological image quality enhancement based on the  generative adversarial network as a foundation for establishing a  multi-institute microscopy cooperative network