  Deep learning methods are becoming widely used for restoration of defects
associated with fluorescence microscopy imaging. One of the major challenges in
application of such methods is the availability of training data. In this work,
we propose a unified method for reconstruction of multi-defect fluorescence
microscopy images when training data is limited. Our approach consists of two
stages: first, we perform data augmentation using Generative Adversarial
Network (GAN) with conditional instance normalization (CIN); second, we train a
conditional GAN (cGAN) on paired ground-truth and defected images to perform
restoration. The experiments on three common types of imaging defects with
different amounts of training data show that the proposed method gives
comparable results or outperforms CARE, deblurGAN and CycleGAN in restored
image quality when available data is limited.
