Medical image information may be polluted by noise in the process of generation and transmission, which will seriously hinder the follow-up image processing and medical diagnosis. In medical images, there is a typical mixed noise composed of additive white Gaussian noise (AWGN) and impulse noise. In the conventional denoising methods, impulse noise is first removed, followed by the elimination of white Gaussian noise (WGN). However, it is difficult to separate the two kinds of noises completely in practical application. The existing denoising algorithm of weight coding based on sparse nonlocal regularization, which can simultaneously remove AWGN and impulse noise, is plagued by the problems of incomplete noise removal and serious loss of details. The denoising algorithm based on sparse representation and low rank constraint can preserve image details better. Thus, a medical image denoising algorithm based on sparse nonlocal regularization weighted coding and low rank constraint is proposed. The denoising effect of the proposed method and the original algorithm on computed tomography (CT) image and magnetic resonance (MR) image are compared. It is revealed that, under different σ and ρ values, the PSNR and FSIM values of CT and MRI images are evidently superior to those of traditional algorithms, suggesting that the algorithm proposed in this work has better denoising effects on medical images than traditional denoising algorithms.

中文翻译：

---

基于稀疏非局部正则化加权编码和低秩约束的医学图像去噪算法

医学图像信息在产生和传输过程中可能会受到噪声的污染，严重阻碍后续的图像处理和医学诊断。在医学图像中，存在由加性高斯白噪声（AWGN）和脉冲噪声组成的典型混合噪声。在传统的去噪方法中，首先去除脉冲噪声，然后去除高斯白噪声（WGN）。然而，在实际应用中很难将这两种噪声完全区分开来。现有的基于稀疏非局部正则化的权重编码去噪算法可以同时去除AWGN和脉冲噪声，存在去噪不彻底、细节丢失严重等问题。基于稀疏表示和低秩约束的去噪算法可以更好地保留图像细节。为此，提出了一种基于稀疏非局部正则化加权编码和低秩约束的医学图像去噪算法。比较了所提出的方法和原始算法对计算机断层扫描（CT）图像和磁共振（MR）图像的去噪效果。据透露，在不同σ和ρ值，CT 和 MRI 图像的 PSNR 和 FSIM 值明显优于传统算法，表明本文提出的算法对医学图像的去噪效果优于传统去噪算法。