# Defocus Deblur Microscopy via Head-to-Tail Cross-scale Fusion

arXiv: https://arxiv.org/abs/2201.02876 · HTML: https://mlsanity.com/paper/2201.02876/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 8 Jan 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Rethinking Coarse-to-Fine Approach in Single Image Deblurring](https://mlsanity.com/paper/2108.05054/) (2021)
- [Multi-Scale Boosted Dehazing Network with Dense Feature Fusion](https://mlsanity.com/paper/2004.13388/) (2020)
- [Iterative Filter Adaptive Network for Single Image Defocus Deblurring](https://mlsanity.com/paper/2108.13610/) (2021)
- [Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning](https://mlsanity.com/paper/1901.11252/) (2019)
- [A New Deep Learning Method for Image Deblurring in Optical Microscopic Systems](https://mlsanity.com/paper/1910.03928/) (2019)
- [Blind deblurring for microscopic pathology images using deep learning networks](https://mlsanity.com/paper/2011.11879/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive Learning](https://mlsanity.com/paper/2403.02611/) (2024)
- [Joint Deblurring and 3D Reconstruction for Macrophotography](https://mlsanity.com/paper/2510.01640/) (2025)

## Abstract (arXiv)

Microscopy imaging is vital in biology research and diagnosis. When imaging at the scale of cell or molecule level, mechanical drift on the axial axis can be difficult to correct. Although multi-scale networks have been developed for deblurring, those cascade residual learning approaches fail to accurately capture the end-to-end non-linearity of deconvolution, a relation between in-focus images and their out-of-focus counterparts in microscopy. In our model, we adopt a structure of multi-scale U-Net without cascade residual leaning. Additionally, in contrast to the conventional coarse-to-fine model, our model strengthens the cross-scale interaction by fusing the features from the coarser sub-networks with the finer ones in a head-to-tail manner: the decoder from the coarser scale is fused with the encoder of the finer ones. Such interaction contributes to better feature learning as fusion happens across decoder and encoder at all scales. Numerous experiments demonstrate that our method yields better performance when compared with other existing models.