Wang Yue, Xue Linlin, Yan Yuqian, Wang Zhongpeng
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Entropy (Basel). 2023 Aug 22;25(9):1248. doi: 10.3390/e25091248.
The measurement matrix used influences the performance of image reconstruction in compressed sensing. To enhance the performance of image reconstruction in compressed sensing, two different Gaussian random matrices were orthogonalized via Gram-Schmidt orthogonalization, respectively. Then, one was used as the real part and the other as the imaginary part to construct a complex-valued Gaussian matrix. Furthermore, we sparsified the proposed measurement matrix to reduce the storage space and computation. The experimental results show that the complex-valued Gaussian matrix after orthogonalization has better image reconstruction performance, and the peak signal-to-noise ratio and structural similarity under different compression ratios are better than the real-valued measurement matrix. Moreover, the sparse measurement matrix can effectively reduce the amount of calculation.
所使用的测量矩阵会影响压缩感知中图像重建的性能。为了提高压缩感知中图像重建的性能，分别通过Gram-Schmidt正交化对两个不同的高斯随机矩阵进行了正交化。然后，将其中一个用作实部，另一个用作虚部来构造一个复值高斯矩阵。此外，我们对提出的测量矩阵进行了稀疏化处理以减少存储空间和计算量。实验结果表明，正交化后的复值高斯矩阵具有更好的图像重建性能，并且在不同压缩比下的峰值信噪比和结构相似性均优于实值测量矩阵。此外，稀疏测量矩阵可以有效减少计算量。
