Wang Shanshan, Xia Yong, Dong Pei, Feng David Dagan, Luo Jianhua, Huang Qiu
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:4030-3. doi: 10.1109/EMBC.2013.6610429.
This paper proposes a spatially adaptive constrained dictionary learning (SAC-DL) algorithm for Rician noise removal in magnitude magnetic resonance (MR) images. This algorithm explores both the strength of dictionary learning to preserve image structures and the robustness of local variance estimation to remove signal-dependent Rician noise. The magnitude image is first separated into a number of partly overlapping image patches. The statistics of each patch are collected and analyzed to obtain a local noise variance. To better adapt to Rician noise, a correction factor is formulated with the local signal-to-noise ratio (SNR). Finally, the trained dictionary is used to denoise each image patch under spatially adaptive constraints. The proposed algorithm has been compared to the popular nonlocal means (NLM) filtering and unbiased NLM (UNLM) algorithm on simulated T1-weighted, T2-weighted and PD-weighted MR images. Our results suggest that the SAC-DL algorithm preserves more image structures while effectively removing the noise than NLM and it is also superior to UNLM at low noise levels.
本文提出了一种空间自适应约束字典学习（SAC-DL）算法，用于去除磁共振（MR）图像幅度中的莱斯噪声。该算法既利用了字典学习在保留图像结构方面的优势，又利用了局部方差估计在去除与信号相关的莱斯噪声方面的稳健性。首先将幅度图像分割成若干部分重叠的图像块。收集并分析每个图像块的统计信息以获得局部噪声方差。为了更好地适应莱斯噪声，利用局部信噪比（SNR）制定了一个校正因子。最后，使用训练好的字典在空间自适应约束下对每个图像块进行去噪。在模拟的T1加权、T2加权和质子密度加权MR图像上，将所提出的算法与流行的非局部均值（NLM）滤波和无偏NLM（UNLM）算法进行了比较。我们的结果表明，SAC-DL算法在有效去除噪声的同时比NLM保留了更多的图像结构，并且在低噪声水平下也优于UNLM。
