Lei Yang, Xu Dong, Zhou Zhengyang, Wang Tonghe, Dong Xue, Liu Tian, Dhabaan Anees, Curran Walter J, Yang Xiaofeng
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
Department of Ultrasound Imaging, Zhejiang Cancer Hospital, Hangzhou, China 310022.
Proc SPIE Int Soc Opt Eng. 2018 Mar;10574. doi: 10.1117/12.2292890.
We propose a denoising method of CT image based on low-rank sparse coding. The proposed method constructs an adaptive dictionary of image patches and estimates the sparse coding regularization parameters using the Bayesian interpretation. A low-rank approximation approach is used to simultaneously construct the dictionary and achieve sparse representation through clustering similar image patches. A variable-splitting scheme and a quadratic optimization are used to reconstruct CT image based on achieved sparse coefficients. We tested this denoising technology using phantom, brain and abdominal CT images. The experimental results showed that the proposed method delivers state-of-art denoising performance, both in terms of objective criteria and visual quality.
我们提出了一种基于低秩稀疏编码的CT图像去噪方法。该方法构建了一个图像块自适应字典，并利用贝叶斯解释估计稀疏编码正则化参数。采用低秩逼近方法同时构建字典，并通过对相似图像块进行聚类来实现稀疏表示。基于得到的稀疏系数，使用变量分裂方案和二次优化来重建CT图像。我们使用体模、脑部和腹部CT图像对这种去噪技术进行了测试。实验结果表明，该方法在客观标准和视觉质量方面均具有领先的去噪性能。
