Abstract
The clarity of microscopic images is vital in biology research and diagnosis. When taking microscopy images at cell or molecule level, mechanical drift occurs and could be difficult and expansive to counter. Such a problem could be overcome by developing an end-to-end deep learning-based workflow capable of predicting in focused microscopic images from out-of-focused counterparts. In our model, we adopt a structure of multi-level U-net, each level connected head-to-tail with corresponding convolution layers from each other. In contrast to the conventional coarse-to-fine model, our model uses the knowledge distilled from the coarse network transferred to the finer network. We evaluate the performance of our model and found our method to be effective and has a better performance by comparing the results with existing models.
Abstract (translated)
URL
https://arxiv.org/abs/2201.02876
