A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid
  Transformer and Contrastive Learning
Yuelin Zhang Pengyu Zheng Wanquan Yan Chengyu Fang Shing Shin Cheng
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
multi-pyramid transformer (MPT) and extended frequency contrastive
regularization (EFCR) is proposed to tackle two outstanding challenges in
microscopy deblur: longer attention span and feature 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 feature 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.