Xu P, Guo L, Feng Y, Zhang X
School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing//Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology//Center for Brain Science and Brain-Inspired Intelligence of Guangdong-Hong Kong-Macao Greater Bay Area, Southern Medical University, Guangzhou 510515, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2021 Aug 31;41(9):1400-1408. doi: 10.12122/j.issn.1673-4254.2021.09.16.
To propose a novel diffusion-weighted (DW) image denoising algorithm based on HOSVD to improve the signal-to-noise ratio (SNR) of DW images and the accuracy of subsequent quantization parameters.
This HOSVDbased denoising method incorporated the sparse constraint and noise-correction model. The signal expectations with Rician noise were integrated into the traditional HOSVD denoising framework for direct denoising of the DW images with Rician noise. HOSVD denoising was performed directly on each local DW image block to avoid the stripe artifacts. We compared the proposed method with 4 image denoising algorithms (LR + Edge, GL-HOSVD, BM3D and NLM) to verify the effect of the proposed method.
The experimental results showed that the proposed method effectively reduced the noise of DW images while preserving the image details and edge structure information. The proposed algorithm was significantly better than LR +Edge, BM3D and NLM in terms of quantitative metrics of PSNR, SSIM and FA-RMSE and in visual evaluation of denoising images and FA images. GL-HOSVD obtained good denoising results but introduced stripe artifacts at a high noise level during the denoising process. In contrast, the proposed method achieved good denoising results without causing stripe artifacts.
This HOSVD-based denoising method allows direct processing of DW images with Rician noise without introducing artifacts and can provide accurate quantitative parameters for diagnostic purposes.
提出一种基于高阶奇异值分解（HOSVD）的新型扩散加权（DW）图像去噪算法，以提高DW图像的信噪比（SNR）及后续量化参数的准确性。
这种基于HOSVD的去噪方法纳入了稀疏约束和噪声校正模型。将带有莱斯噪声的信号期望整合到传统的HOSVD去噪框架中，对带有莱斯噪声的DW图像进行直接去噪。直接对每个局部DW图像块进行HOSVD去噪，以避免条纹伪影。我们将所提出的方法与4种图像去噪算法（LR + Edge、GL-HOSVD、BM3D和NLM）进行比较，以验证所提方法的效果。
实验结果表明，所提方法在保留图像细节和边缘结构信息的同时，有效降低了DW图像的噪声。在所提算法的PSNR、SSIM和FA-RMSE定量指标以及去噪图像和FA图像的视觉评估方面，显著优于LR + Edge、BM3D和NLM。GL-HOSVD获得了良好的去噪结果，但在去噪过程中在高噪声水平下引入了条纹伪影。相比之下，所提方法在不产生条纹伪影的情况下取得了良好的去噪效果。
这种基于HOSVD的去噪方法能够直接处理带有莱斯噪声的DW图像而不引入伪影，并可为诊断目的提供准确的定量参数。
