稀疏化的压缩传感超声图像重构特性研究

传统医学超声成像射频信息存储数据量大,传输耗时且成本高.基于稀疏化的压缩传感技术,研究超声图像的稀疏特性,利用离散余弦变换(DCT)对原始超声图像进行稀疏变换,在稀疏域内进行压缩采样获得降采样数据,然后通过求解L1范数重构原始图像.理论和实验表明,该方法可以很好地应用到超声图像的重构中,且随着稀疏度的减小和测量值的增加,重构图像的峰值信噪比(PSNR值)增加,重构图像的视觉效果较好.

The performance of reconstruction ultrasound imaging based on compressed sensing by sparsity

The traditional ultrasound imaging has a large amount of data, and its process would take a long time. We reconstructed the ultrasound imaging based on the compressed sensing method by the sparsity, and studied the sparsity performance of the reconstruction ultrasound image. The original image was transformed by the discrete cosine transform (DCT), then the sampling frequency data was obtained by compressed sampling on the sparsity domain, and finally, we reconstructed the original image by solving L-1 norm. The theories and experimental results show that the method in the paper could be used in reconstruction of ultrasound images, and its peak signal to noise ratio (PSNR) value of the reconstruction image increases as the sparsity decreases and the measurements value increases.