Fast Gradient-Based Algorithm for Total Variation Regularized Tomography Reconstruction
For tomography reconstruction, the iteration method based on TV (total variation) regularization is proven effective especially when projection data is insufficient or noisy due to low count levels. The purpose of this paper is to develop a fast iterative algorithm to solve this recently popular TVregularized CT optimization problem. Using the method of surrogate function, we split the sum minimization scheme to two sub problems: minimizing weighted least square function and TV denoising with weighted norm. The conventional projection iterative methods such as ART, SART are applicable for the first sub problem. For the second one, we adopt Chambolles orthogonal projection scheme to avoid numerical difficulty due to the non differentiability of the TV norm. Then the solution can be obtained using the alternative iterative minimization algorithm. The proposed approach is applied to fan-beam CT with few-view data and the experiment result indicate that this approach is faster than gradient descent based TV algorithms and reconstruction image is better with same iterations. In conclusion, the proposed uncoupled algorithm is stable and efficient and it can be extended to cone-beam CT reconstruction and interior tomography easily.
surrogate functions total varition regularization tomographic reconstruction
Wang Li-yan Wei Zhi-hui
Mathematics Department Southeast University Nanjing, China Computer Science Department Nanjing Unive Computer Science Department Nanjing University of Science & Technology Nanjing, China
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
1601-1605
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）