稀疏正则化方法的超声信号反卷积

提出了一种在稀疏分解框架下的超声信号反卷积模型，改善了超声成像的质量。该模型包含两个正则项，分别约束信号的光滑性和字典表示的稀疏性，并应用高阶统计量和MA模型估计系统的点扩散函数。模型直接求解很困难，采用分裂Bregman方法交替迭代求解；并对反卷积的信号进行动态滤波、包络检波、二次抽样、动态压缩、灰阶映射等处理，得到超声灰度图像。实验结果表明，该反卷积方法成像比直接成像的分辨率高，图像的对比度得到增强，斑点噪声明显减少。

Sparse Regularization-Based Ultrasound Signal Deconvolution

A ultrasound signal deconvolution model in the framework of the sparse decomposition is proposed to improve the quality of medical ultrasound images. The smoothness of the signal and the sparsity of the dictionary representation are constrained by using two regularization terms, and the point spread function is estimated by using higher order statistics and MA model. The proposed model is solved by alternatively iterating split Bregman method. The gray scale ultrasound image is acquired by the dynamic filtering, envelope detecting, second sampling, dynamic compressing, and gray scale mapping. Experiments show that the proposed deconvolution method can achieve images with higher resolution, better contrast enhancement, and less speckle noise, compared with direct imaging methods.