Convolutional Neural Networks for Spherical Signal Processing via Area-Regular Spherical Haar Tight Framelets

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Online published29 Mar 2022
Publication statusOnline published - 29 Mar 2022


In this article, 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 two spheres and establish its corresponding spherical Haar tight framelets with directionality. We conclude by evaluating and illustrate 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 fast framelet decomposition and reconstruction algorithms. Experiment results show that our proposed CNN model outperforms threshold methods and processes strong generalization and robustness.

Research Area(s)

  • Area regular, bounded domains, Convolution, convolutional neural network (CNN), Convolutional neural networks, directional framelets, image denoising, Noise reduction, Robustness, Signal denoising, Signal representation, spherical Haar framelets, spherical signals, tight framelets., Urban areas