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.

翻译：散焦模糊是显微成像中持续存在的问题，对细胞显微术和显微手术中的病理学解读及医学干预构成危害。针对该问题，提出一种包含多金字塔Transformer（MPT）与扩展频率对比正则化（EFCR）的统一框架，以解决显微去模糊中两项突出挑战：更长注意力跨度与数据不足。MPT在每个网络阶段采用显式金字塔结构，集成跨尺度窗口注意力（CSWA）、尺度内通道注意力（ISCA）和特征增强前馈网络（FEFN），以捕获长距离跨尺度空间交互与全局通道上下文。EFCR通过探索不同频段的潜在去模糊信号解决数据不足问题，同时实现去模糊知识迁移，从额外数据中学习跨域信息，提升标注与未标注数据的去模糊性能。大量实验与下游任务验证表明，该框架在多个数据集上达到最优性能。项目主页：https://github.com/PieceZhang/MPT-CataBlur。