论文标题
通过头到尾跨尺度融合的DeDocus Deblur显微镜
Defocus Deblur Microscopy via Head-to-Tail Cross-scale Fusion
论文作者
论文摘要
显微镜成像在生物学研究和诊断中至关重要。当在细胞或分子水平的尺度上进行成像时，轴向轴上的机械漂移很难纠正。尽管已经开发出用于去膨胀的多尺度网络，但这些级联残留学习方法无法准确捕获反向卷积的端到端非线性，这是焦点内图像与显微镜中焦点的相关性之间的关系。在我们的模型中，我们采用了一个多尺度U-NET的结构，而无需级联残留倾斜。此外，与常规的粗到精细模型相反，我们的模型通过以易于面对的方式将较粗糙子网络的特征与较细的特征融合在一起，从而增强了跨尺度相互作用：从较粗的尺度的解码器与较细的编码器融合在一起。这种相互作用有助于更好的特征学习，因为融合在各个尺度上都在解码器和编码器之间进行。许多实验表明，与其他现有模型相比，我们的方法可以产生更好的性能。
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.
