Blind deblurring for microscopic pathology images using deep learning networks

TL;DR

This paper presents a deep learning method for blind deblurring of microscopic pathology images, improving image clarity and diagnostic accuracy without prior knowledge of blur characteristics.

Contribution

The study introduces an end-to-end deep learning framework combining blur classification and deblurring networks, outperforming traditional methods in pathology image restoration.

Findings

Effective blur type identification with high accuracy

Significant enhancement in image sharpness and detail retrieval

Improved diagnostic performance of AI algorithms on deblurred images

Abstract

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.