基于协同全变分的高光谱图像绘制

2017 IEEE International Conference on Image Processing (ICIP) Pub Date : 2017-09-16 DOI:10.1109/ICIP.2017.8297090

P. Addesso, M. Mura, Laurent Condat, R. Restaino, G. Vivone, Daniele Picone, J. Chanussot

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引用次数: 13

摘要

高光谱图像的图像修复是一个具有挑战性的研究领域，近年来已经开发了几种方法来处理这类数据。本文通过一种基于协同全变分(CTV)的正则化项凸优化技术来解决缺失数据的恢复问题。特别地，我们评估了几种CTV实例与不同降维算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Hyperspectral image inpainting based on collaborative total variation

Inpainting in hyperspectral imagery is a challenging research area and several methods have been recently developed to deal with this kind of data. In this paper we address missing data restoration via a convex optimization technique with regularization term based on Collaborative Total Variation (CTV). In particular we evaluate the effectiveness of several instances of CTV in conjunction with different dimensionality reduction algorithms.

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2017 IEEE International Conference on Image Processing (ICIP)

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