宫颈细胞病理学图像重新聚焦对于解决整个幻灯片图像中的散焦模糊问题很重要。然而,目前大多数的去模糊方法是为全局运动模糊而开发的,而不是局部散焦模糊,并且需要对看不见的域进行大量的监督重新训练。在本文中,我们提出了一种通过多尺度注意特征和领域归一化对宫颈细胞病理学图像进行重聚焦的方法。我们的方法由域归一化网(DNN)和重聚焦网(RFN)组成。在DNN中,我们采用无注册循环方案将未见的无监督域归一化到可见的监督域,并引入灰色掩模损失和色调编码掩模损失,以确保细胞结构和基本色调的一致性。在RFN中,结合散焦模糊的局部性和稀疏性特征,我们设计了一个多尺度重聚焦网络来增强细胞核和细胞质的重建,并引入散焦强度估计掩模来加强局部模糊的重建。我们在监督域和无监督域上集成了混合学习策略,以使RFN在无监督域上实现良好的重新聚焦。我们建立了宫颈细胞病理学图像重聚焦数据集,并进行了广泛的实验,以证明与当前去模糊技术模型相比,我们方法的优越性。此外,我们证明了重新聚焦的图像有助于提高后续高级分析任务的性能。我们发布了重新聚焦的数据集和源代码,以促进该领域的发展。
Cervical cytopathology image
refocusing is important for addressing the problem of defocus blur in whole slide images. However, most of current deblurring methods are developed for global motion blur instead of local defocus blur and need a lot of supervised re-training for unseen domains. In this paper, we propose a
refocusing method for cervical cytopathology images via multi-scale attention features and domain normalization. Our method consists of a domain normalization net (DNN) and a
refocusing net (RFN). In DNN, we adopt registration-free cycle scheme for normalizing the unseen unsupervised domains into the seen supervised domain and introduce gray mask loss and hue-encoding mask loss to ensure the consistency of cell structure and basic hue. In RFN, combining the locality and sparseness characteristics of defocus blur, we design a multi-scale refocusing network to enhance the reconstruction of cell nucleus and cytoplasm, and introduce defocus intensity estimation mask to strengthen the reconstruction of local blur. We integrate hybrid learning strategy on the supervised and unsupervised domains to make RFN achieving well refocusing on the unsupervised domain. We build a cervical cytopathology image
refocusing dataset and conduct extensive experiments to demonstrate the superiority of our method compared with current deblurring state-of-the-art models. Furthermore, we prove that the refocused images help improve the performance of subsequent high-level analysis tasks. We release the
refocusing dataset and source codes to promote the development of this field.