联合稀疏和低秩表示的医学超声图像去噪

为了保持超声图像的边缘和细节特征，同时去除图像中的噪声，提出了一种改进的低秩稀疏矩阵分解模型。首先，通过对数变换将乘性性质的斑点噪声转换为加性噪声；然后引入L1范数和改进的低秩正则项，以最小化保真项、正则项为目标函数，迭代恢复出去噪后的超声图像；最后使用指数变换从对数域中还原。将本模型应用于肿瘤超声图像，与一些经典的去噪算法进行比较，得出该模型对消化道粘膜下肿瘤超声图像去噪估计具有良好的适用性和实时性。

Medical ultrasound image denoising based on low-rank and sparse model

An improved low-rank and sparse matrix decomposition model was proposed to preserve edge, detail features and remove noise in ultrasound images. Firstly, logarithmic transformation was carried out to transform the multiplicative speckle noise into additive noise. Then the L1-norm and improved low-rank regularizer term were introduced to iteratively recover the denoised ultrasound image with the minimisation of the fidelity and regularization term as the objective function. Finally, the exponential transformation was used to restore the resulting graph from the logarithmic domain. The model was compared with some classical denoising algorithms using tumour ultrasound images. The results show that the model has good applicability and real time for the estimation of ultrasonic image denoising of gastrointestinal submucosal tumours.