E3S Web Conf.
Volume 269, 20212021 International Conference on Environmental Engineering, Agricultural Pollution and Hydraulical Studies (EEAPHS 2021)
|Number of page(s)||10|
|Published online||09 June 2021|
l1/2 regularization for wavelet frames based few-view CT reconstruction
Chongqing Key Laboratory of Complex Data Analysis & Artificial Intelligence, Chongqing University of Arts and Sciences, Chongqing, 402160, China
2 Chongqing Key Laboratory of Group & Graph Theories and Applications, Chongqing University of Arts and Sciences, Chongqing, 402160, China
3 Chongqing College of Electronic Engineering, Department of Physical Education and Defense Education, Chongqing, 401331, China
4 Key Lab for OCME, School of Mathematical Sciences, Chongqing Normal University, Chongqing, 400047, China
5 Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, 400044, China
* Corresponding author: firstname.lastname@example.org
Reducing the radiation exposure in computed tomography (CT) is always a significant research topic in radiology. Image reconstruction from few-view projection is a reasonable and effective way to decrease the number of rays to lower the radiation exposure. But how to maintain high image reconstruction quality while reducing radiation exposure is a major challenge. To solve this problem, several researchers are absorbed in l0 or l1 regularization based optimization models to deal with it. However, the solution of l1 regularization based optimization model is not sparser than that of l1/2 or l0 regularization, and solving the l0 regularization is more difficult than solving the l1/2 regularization. In this paper, we develop l1/2 regularization for wavelet frames based image reconstruction model to research the few-view problem. First, the existence of the solution of the corresponding model is demonstrated. Second, an alternate direction method (ADM) is utilized to separate the original problem into two subproblems, where the former subproblem about the image is solved using the idea of the proximal mapping, the simultaneous algebraic reconstruction technique (SART) and the projection and contraction (PC) algorithm, and the later subproblem about the wavelet coefficients is solved using the half thresholding (HT) algorithm. Furthermore, the convergence analysis of our method is given by the simulated implementions. Simulated and real experiments confirm the effectiveness of our method.
© The Authors, published by EDP Sciences, 2021
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