Chengyi University College, Jimei University, Xiamen, China.
School of Software Engineering, Tongji University, Shanghai, China.
Comput Math Methods Med. 2020 Apr 1;2020:1405647. doi: 10.1155/2020/1405647. eCollection 2020.
Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. This paper puts forward a hybrid denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all the denoised subimages to obtain the denoised MR image. The experimental results show that the proposed method has significantly better performance in terms of mean square error and peak signal-to-noise ratio than each method alone.
磁共振（MR）图像经常受到高斯噪声的污染，这种噪声是由电子元件的随机热运动引起的电子噪声，会降低图像的质量和可靠性。本文提出了一种基于两个稀疏表示形态分量和一个残差部分的混合去噪算法。首先，通过 MCA 将含噪的 MR 图像分解为卡通、纹理和残差部分，然后分别使用维纳滤波器、小波硬阈值和小波软阈值对每个部分进行去噪。最后，将所有去噪后的子图像堆叠起来，得到去噪后的 MR 图像。实验结果表明，与单独使用每种方法相比，所提出的方法在均方误差和峰值信噪比方面具有显著更好的性能。
