文献类型：期刊文献

英文题名：High Accuracy Compressive Chromo-Tomography Reconstruction via Convolutional Sparse Coding

作者：Li, Baoping[1]; Zhang, Xuesong[1]; Jiang, Jing[2]; Chen, Yuzhong[1]; Zhang, Qi[1]; Ming, Anlong[1]

第一作者：Li, Baoping

机构：[1] Beijing University of Posts and Telecommunications, Beijing, 100876, China; [2] Beijing Union University, Department of Communication Engineering, Beijing, 100101, China

第一机构：Beijing University of Posts and Telecommunications, Beijing, 100876, China

年份：2020

卷号：2020-July

外文期刊名：Proceedings - IEEE International Conference on Multimedia and Expo

收录：EI(收录号：20203709163267)

语种：英文

外文关键词：Computer vision - Spectroscopy - Tomography - Hyperspectral imaging - Convolution - Numerical methods

摘要：Over the last decade various compressive snapshot hyperspectral imaging methods have been proposed. The limited reconstruction quality from severely compressed measurements, however, has been a practical barrier to real applications. This paper proposes a compressive chromo-tomography framework that incorporates the convolutional sparse coding (CSC) prior into the classical total variation and L1 regularization functionals. Such a combination allows excellent high-frequency recovery capabilities of CSC, while effectively suppressing ghost artifacts in tomographic reconstructions. Since nondifferentiable regularizers are employed, we propose a preconditioned alternating direction method of multipliers (ADMM) for flexible and efficient solutions, both for the reconstruction task and for hyperspectral convolutional dictionary learning. We demonstrate in our numerical experiments that just 25 learned 3D CSC filters can fulfill a rather effective hyperspectral imagery representation and that the proposed method is capable of high accuracy reconstructions. ? 2020 IEEE.