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一个新的复杂值高斯测量矩阵用于图像压缩传感.
Yue Wang1, Linlin Xue1, Yuqian Yan1
1School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
正角化高斯随机矩阵创建了一个复杂值矩阵,改进了压缩传感图像重建. 这种稀疏,复杂的矩阵提高了性能,并减少了与实值矩阵相比的计算负载.
科学领域:
- 信号处理 信号处理
- 图像重建 图像的重建
- 压缩感应 压缩感应
背景情况:
- 压缩感应 (CS) 图像重建性能受到测量矩阵的显著影响.
- 开发先进的测量矩阵对于提高CS成像准确性和效率至关重要.
研究的目的:
- 通过提出一个新的复杂值高斯测量矩阵来增强压缩传感图像重建.
- 调查正角化和散散化对拟议测量矩阵性能的影响.
主要方法:
- 两个不同的高斯随机矩阵使用格拉姆-施密特过程进行了正角化.
- 一个复杂值的高斯矩阵是使用正交矩阵作为真实和虚构元件来构建的.
- 拟议的测量矩阵被分散,以减少存储和计算需求.
主要成果:
- 与实值矩阵相比,正交的复杂值高斯矩阵表现出优越的图像重建性能.
- 通过各种压缩比的复杂矩阵实现了更高的峰值信号噪声比 (PSNR) 和结构相似性 (SSIM).
- 分散测量矩阵有效降低了计算复杂性.
结论:
- 正角化复杂值的高斯矩阵为压缩传感图像重建提供了显著的优势.
- 拟议的方法提供了一种可行的方法来提高CS成像中的图像质量和计算效率.
- 分散是一种优化测量矩阵存储和处理需求的有效策略.
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