  Artificial Intelligence (AI)-powered pathology is a revolutionary step in the
world of digital pathology and shows great promise to increase both diagnosis
accuracy and efficiency. However, defocus and motion blur can obscure tissue or
cell characteristics hence compromising AI algorithms'accuracy and robustness
in analyzing the images. In this paper, we demonstrate a deep-learning-based
approach that can alleviate the defocus and motion blur of a microscopic image
and output a sharper and cleaner image with retrieved fine details without
prior knowledge of the blur type, blur extent and pathological stain. In this
approach, a deep learning classifier is first trained to identify the image
blur type. Then, two encoder-decoder networks are trained and used alone or in
combination to deblur the input image. It is an end-to-end approach and
introduces no corrugated artifacts as traditional blind deconvolution methods
do. We test our approach on different types of pathology specimens and
demonstrate great performance on image blur correction and the subsequent
improvement on the diagnosis outcome of AI algorithms.
