A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive Learning

TL;DR

This paper introduces a unified microscopy deblur framework combining multi-pyramid transformer and contrastive learning to effectively address long-range attention and data scarcity, achieving state-of-the-art results.

Contribution

It proposes a novel multi-pyramid transformer architecture with extended frequency contrastive regularization for improved microscopy defocus deblurring.

Findings

Achieves state-of-the-art deblurring performance on multiple datasets.

Effectively handles data deficiency through frequency-based contrastive learning.

Enhances long-range spatial interaction with a multi-scale transformer design.

Abstract

Defocus blur is a persistent problem in microscope imaging that poses harm to pathology interpretation and medical intervention in cell microscopy and microscope surgery. To address this problem, a unified framework including the multi-pyramid transformer (MPT) and extended frequency contrastive regularization (EFCR) is proposed to tackle two outstanding challenges in microscopy deblur: longer attention span and data deficiency. The MPT employs an explicit pyramid structure at each network stage that integrates the cross-scale window attention (CSWA), the intra-scale channel attention (ISCA), and the feature-enhancing feed-forward network (FEFN) to capture long-range cross-scale spatial interaction and global channel context. The EFCR addresses the data deficiency problem by exploring latent deblur signals from different frequency bands. It also enables deblur knowledge transfer to learn cross-domain information from extra data, improving deblur performance for labeled and unlabeled data. Extensive experiments and downstream task validation show the framework achieves state-of-the-art performance across multiple datasets. Project page: https://github.com/PieceZhang/MPT-CataBlur.