论文标题
双周期：使用Cyclean的自我监督双视荧光显微镜图像重建
Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN
论文作者
论文摘要
三维荧光显微镜通常遭受各向异性的影响，沿轴向方向的分辨率低于横向成像平面内的分辨率。我们通过介绍双周期来解决这个问题，这是一个新的双视荧光图像关节反卷积和融合的框架。受到最近的神经清性方法的启发，双周期被设计为一种循环一致的生成网络，该网络通过将双视发电机和先前引导的退化模型结合使用，以自我监督的方式训练。我们在没有任何外部培训数据的情况下验证了综合数据和真实数据的双周期。
Three-dimensional fluorescence microscopy often suffers from anisotropy, where the resolution along the axial direction is lower than that within the lateral imaging plane. We address this issue by presenting Dual-Cycle, a new framework for joint deconvolution and fusion of dual-view fluorescence images. Inspired by the recent Neuroclear method, Dual-Cycle is designed as a cycle-consistent generative network trained in a self-supervised fashion by combining a dual-view generator and prior-guided degradation model. We validate Dual-Cycle on both synthetic and real data showing its state-of-the-art performance without any external training data.
