一种在扩散加权图像降噪中的算法

扩散加权图像中的噪声为莱斯噪声并且图像本身含有丰富的边界信息，因而要求对DWI图像有效降噪的同时，能够较好地保留图像的边界信息。由于BEMD算法可将图像分解为细节图像及余项图像，其所分解的细节图像包括DWI图像的边界信息以及主噪声，而余项图像则描述图像的趋势信息。因此，提出一种将二维经验模态分解算法与改进的维纳滤波器相结合的降噪算法，并将该算法应用于DWI图像的降噪中。通过实验，将所提出的算法与其他算法应用于DWI图像的降噪处理，并通过对结果的分析比较证明所提出的算法能够更有效地对DWI图像进行降噪处理。

WDWI Denoising Method Based on BEMD and Adaptive Wiener Filter

Diffusion weighted image (DWI) is multi-boundary in nature, and it is particularly important to get accurate boundary signals of DWI . A new method combing bidimensional empirical mode decomposition (BEMD) with modified adaptive Wiener filter is proposed. Through BEMD method the degraded image is decomposed into a detail part and a residual part. The detail part of the image contains the boundary signal and the noise of the degraded image, and the residual part describes the image tendency. Thus, the DWI data is decomposed by BEMD method at first, and then, the modified adaptive Wiener filter is applied to remove the noise in the detail part of DWI and the residual part is thus handled. Finally, the denoised detail image and its residual are combined to form the denoised DWI. The method is performed on real DWI data. Experiment results positively show that the proposed method removes noise effectively and keeps the boundary of DWI successfully.