压缩感知与多图像融合:一种信息论方法

Kamran Keykhosravi, S. Mashhadi
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引用次数: 0

摘要

在本文中,我们提出了一种信息理论方法来融合压缩感知(CS)技术压缩后的图像。目标是直接使用测量融合多个压缩图像,并仅重建最终图像一次。由于重建是最昂贵的步骤,这将是一个更经济的方法比单独重建每个图像。该方案基于对输入量的测量值进行加权平均计算的结果,其中权重由信息理论函数计算。仿真结果表明,该方法产生的最终图像质量优于传统方法,特别是当输入图像数量超过两个时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Compressed sensing and multiple image fusion: An information theoretic approach
In this paper, we propose an information theoretic approach to fuse images compressed by compressed sensing (CS) techniques. The goal is to fuse multiple compressed images directly using measurements and reconstruct the final image only once. Since the reconstruction is the most expensive step, it would be a more economic method than separate reconstruction of each image. The proposed scheme is based on calculating the result using weighted average on the measurements of the inputs, where weights are calculated by information theoretic functions. The simulation results show that the final images produced by our method have higher quality than those produced by traditional methods, especially if the number of input images exceeds two.
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