面向压缩感知与重构的感知矩阵设计

2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) Pub Date : 2018-02-01 DOI:10.1109/ICAECC.2018.8479466

H B Sharanabasaveshwara, Santosh M. Herur

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引用次数: 2

摘要

压缩采样技术是一种新兴的采样技术，它以亚奈奎斯特速率重建稀疏信号。压缩感知中的一个问题是感知矩阵的设计。随机传感矩阵在得到广泛应用的同时，也存在许多缺点。本文设计了一种新的确定性随机感知矩阵，并在256 × 256的图像上进行了测试。结果表明，与随机感知矩阵相比，重构时间提高24%。由于矩阵是确定性的，因此存储需求小于随机感知矩阵。

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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Designing of Sensing Matrix for Compressive Sensing and Reconstruction

The compressive sampling technique is an emerging sampling technique that reconstructs a sparse signal at sub-Nyquist rate. One of the concerns in compression sensing is design of sensing matrix. While random sensing matrix are widely in use, they have many disadvantages. In this paper a novel Deterministic Random Sensing Matrix is designed and tested on image of size 256 × 256. The result shows 24% improvement in reconstruction time over Random Sensing Matrix. Since the matrix is deterministic the storage requirement is less than the Random Sensing Matrix.

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2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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