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中文摘要：
针对低剂量计算机断层扫描（CT）重建图像时出现明显条形伪影的现象,提出一种自适应广义总变分（ATGV）降噪算法。该算法考虑了传统广义总变分（TGV）算法在降噪时模糊图像边缘信息的缺点,把可以有效区分图像平滑区和细节区的直觉模糊熵应用到传统TGV中,对图像的不同区域进行不同强度的去噪,从而达到保护图像细节的效果。该算法首先采用滤波反投影（FBP）算法得到低剂量CT重建图像;然后利用基于直觉模糊熵的边缘指示函数对传统TGV模型进行改进;最后用改进后的模型对重建图像进行降噪处理。采用Shepp-Logan模型和数字胸腔模型（thorax phantom）仿真低剂量CT重建图像来验证算法的有效性。实验结果表明,所提算法的归一化均方距离（NMSD）和归一化平均绝对距离（NAAD）均比总变分（TV）降噪算法和广义总变分（TGV）降噪算法小,且可分别获得26.90 d B和44.58 d B的峰值信噪比（PSNR）。该算法在去除条形伪影的同时可以较好地保持图像的边缘和细节信息。
英文摘要：
A new denoising algorithm, Adaptive Total Generalized Variation（ ATGV）, was proposed for removing streak artifacts within the reconstructed image of low-dose Computed Tomography（ CT）. Considering the shortage that the traditional Total Generalized Variation（ TGV） would blur the edge details, the intuitionistic fuzzy entropy which can distinguish the smooth and detail regions was introduced into the TGV algorithm. Different areas of the image were processed with different denoising intensities. As a result, the image details could be well preserved. Firstly, the Filtered Back Projection（ FBP）algorithm was used to obtain a reconstructed image. Secondly, the edge indicator function based on intuitive fuzzy entropy was applied to improve the TGV algorithm. Finally, the new algorithm was employed to reduce the noise in the reconstructed image. The simulations of the low-dose CT image reconstruction for the Shepp-Logan model and the thorax phantom were used to test the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm has the smaller values of the Normalized Mean Square Distance（ NMSD） and Normalized Average Absolute Distance（ NAAD） in the two experiment images, compared with the Total Variation（ TV） algorithm and TGV algorithm. Meanwhile, the two experiment images processed with the new method can obtain high Peak Signal-to-Noise Ratios（ PSNR） of 26. 90 d B and 44. 58 d B,respectively. So the proposed algorithm can effectively preserve image details and edges, while reducing streak artifacts.
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