# Blind deblurring for microscopic pathology images using deep learning networks

arXiv: https://arxiv.org/abs/2011.11879 · HTML: https://mlsanity.com/paper/2011.11879/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive Learning](https://mlsanity.com/paper/2403.02611/) (2024)
- [Histopath-C: Towards Realistic Domain Shifts for Histopathology Vision-Language Adaptation](https://mlsanity.com/paper/2601.12493/) (2026)
- [Defocus Deblur Microscopy via Head-to-Tail Cross-scale Fusion](https://mlsanity.com/paper/2201.02876/) (2022)
- [Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey](https://mlsanity.com/paper/2509.15363/) (2025)
- [Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space](https://mlsanity.com/paper/2307.15461/) (2023)
- [Joint Deblurring and 3D Reconstruction for Macrophotography](https://mlsanity.com/paper/2510.01640/) (2025)

## Abstract (arXiv)

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.