Gao Mei, Kang Baosheng, Feng Xiangchu, Zhang Wei, Zhang Wenjuan
School of Information Science and Technology, Northwest University, Xi'an 710127, China.
School of Science, Xi'an Technological University, Xi'an 710021, China.
Sensors (Basel). 2019 Jul 18;19(14):3164. doi: 10.3390/s19143164.
Multiplicative speckle noise removal is a challenging task in image processing. Motivated by the performance of anisotropic diffusion in additive noise removal and the structure of the standard deviation of a compressed speckle noisy image, we address this problem with anisotropic diffusion theories. Firstly, an anisotropic diffusion model based on image statistics, including information on the gradient of the image, gray levels, and noise standard deviation of the image, is proposed. Although the proposed model can effectively remove multiplicative speckle noise, it does not consider the noise at the edge during the denoising process. Hence, we decompose the divergence term in order to make the diffusion at the edge occur along the boundaries rather than perpendicular to the boundaries, and improve the model to meet our requirements. Secondly, the iteration stopping criteria based on kurtosis and correlation in view of the lack of ground truth in real image experiments, is proposed. The optimal values of the parameters in the model are obtained by learning. To improve the denoising effect, post-processing is performed. Finally, the simulation results show that the proposed model can effectively remove the speckle noise and retain minute details of the images for the real ultrasound and RGB color images.
乘性散斑噪声去除是图像处理中的一项具有挑战性的任务。受各向异性扩散在加性噪声去除中的性能以及压缩散斑噪声图像标准差结构的启发，我们用各向异性扩散理论来解决这个问题。首先，提出了一种基于图像统计信息的各向异性扩散模型，该信息包括图像梯度、灰度级以及图像噪声标准差。虽然所提出的模型能够有效地去除乘性散斑噪声，但在去噪过程中没有考虑边缘处的噪声。因此，我们对散度项进行分解，以使边缘处的扩散沿边界发生而不是垂直于边界，并改进模型以满足我们的要求。其次，鉴于实际图像实验中缺乏真实的基准，提出了基于峰度和相关性的迭代停止准则。通过学习获得模型中参数的最优值。为了提高去噪效果，进行了后处理。最后，仿真结果表明，所提出的模型能够有效地去除散斑噪声，并为真实超声图像和RGB彩色图像保留图像的细微细节。
