使用孪生神经网络的自监督单图像去卷积
Self-Supervised Single-Image Deconvolution with Siamese Neural Networks
AI总结：
针对自监督盲点去卷积中大卷积核训练困难的问题，本文引入快速傅里叶变换卷积和孪生不变性损失，提升三维显微去卷积训练速度并取得优于现有方法的性能。
摘要：
图像重建中的逆问题因未知噪声特性而从根本上变得复杂。经典迭代去卷积方法会放大噪声，并需要谨慎选择参数，以在锐度和颗粒感之间实现最佳折中。深度学习方法允许对噪声进行灵活参数化，并直接从数据中学习其特性。近来，通过在端到端训练中纳入已知点扩散函数，自监督盲点神经网络已被成功用于图像去卷积。然而，由于其意味着难以优化的大卷积核，这些方法的实际应用一直局限于生物医学领域的二维图像。我们利用快速傅里叶变换卷积来解决这一问题，该卷积可在三维显微去卷积任务中提升训练速度。此外，我们提出将孪生不变性损失用于去卷积，并通过实验确定其在神经网络中位于盲点分支与完整图像分支之间的最佳位置。实验结果表明，我们改进后的框架在已知点扩散函数的情况下优于以往最先进的去卷积方法。
英文摘要：
Inverse problems in image reconstruction are fundamentally complicated by unknown noise properties. Classical iterative deconvolution approaches amplify noise and require careful parameter selection for an optimal trade-off between sharpness and grain. Deep learning methods allow for flexible parametrization of the noise and learning its properties directly from the data. Recently, self-supervised blind-spot neural networks were successfully adopted for image deconvolution by including a known point-spread function in the end-to-end training. However, their practical application has been limited to 2D images in the biomedical domain because it implies large kernels that are poorly optimized. We tackle this problem with Fast Fourier Transform convolutions that provide training speed-up in 3D microscopy deconvolution tasks. Further, we propose to adopt a Siamese invariance loss for deconvolution and empirically identify its optimal position in the neural network between blind-spot and full image branches. The experimental results show that our improved framework outperforms the previous state-of-the-art deconvolution methods with a known point spread function.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
