论文标题
卷积神经网络，用于球形信号处理，通过球形HAAR紧密的三角形
Convolutional Neural Networks for Spherical Signal Processing via Spherical Haar Tight Framelets
论文作者
论文摘要
在本文中，我们开发了一个一般的理论框架，用于在任何带有层次分区的紧凑型设置上构建HAAR型紧密的印象。特别是，我们在2个球体上构建了一个新型区域规范的层次分区，并以方向性建立了相应的球形HAAR紧密框架。我们通过评估和说明在几个脱氧实验中评估和说明我们面积的球形HAAR紧密印象的有效性来得出结论。此外，我们提出了一个用于球形信号denoisising的卷积神经网络（CNN）模型，该模型采用快速帧分解和重建算法。实验结果表明，我们提出的CNN模型的表现优于阈值方法，并处理强大的概括和鲁棒性。
In this paper, we develop a general theoretical framework for constructing Haar-type tight framelets on any compact set with a hierarchical partition. In particular, we construct a novel area-regular hierarchical partition on the 2-sphere and establish its corresponding spherical Haar tight framelets with directionality. We conclude by evaluating and illustrating the effectiveness of our area-regular spherical Haar tight framelets in several denoising experiments. Furthermore, we propose a convolutional neural network (CNN) model for spherical signal denoising which employs the fast framelet decomposition and reconstruction algorithms. Experiment results show that our proposed CNN model outperforms threshold methods, and processes strong generalization and robustness properties.
