Affiliations: [a] Chongqing Key Laboratory of Complex Data Analysis & Artificial Intelligence, Chongqing University of Arts and Sciences, Chongqing, China
| [b] Chongqing Key Laboratory of Group & Graph Theories and Applications, Chongqing University of Arts and Sciences, Chongqing, China
| [c] Key Lab for OCME, School of Mathematical Sciences, Chongqing Normal University, Chongqing, China
| [d] Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, China
Correspondence:
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Corresponding author: Lingli Zhang, PhD., Chongqing Key Laboratory of Complex Data Analysis & Artificial Intelligence, Chongqing University of Arts and Sciences, Chongqing, 402160, China. E-mail: [email protected].
Abstract: BACKGROUND AND OBJECTIVE:Since the stair artifacts may affect non-destructive testing (NDT) and diagnosis in the later stage, an applicable model is desperately needed, which can deal with the stair artifacts and preserve the edges. However, the classical total variation (TV) algorithm only considers the sparsity of the gradient transformed image. The objective of this study is to introduce and test a new method based on group sparsity to address the low signal-to-noise ratio (SNR) problem. METHODS:This study proposes a weighted total variation with overlapping group sparsity model. This model combines the Gaussian kernel and overlapping group sparsity into TV model denoted as GOGS-TV, which considers the structure sparsity of the image to be reconstructed to deal with the stair artifacts. On one hand, TV is the accepted commercial algorithm, and it can work well in many situations. On the other hand, the Gaussian kernel can associate the points around each pixel. Quantitative assessments are implemented to verify this merit. RESULTS:Numerical simulations are performed to validate the presented method, compared with the classical simultaneous algebraic reconstruction technique (SART) and the state-of-the-art TV algorithm. It confirms the significantly improved SNR of the reconstruction images both in suppressing the noise and preserving the edges using new GOGS-TV model. CONCLUSIONS:The proposed GOGS-TV model demonstrates its advantages to reduce stair artifacts especially in low SNR reconstruction because this new model considers both the sparsity of the gradient image and the structured sparsity. Meanwhile, the Gaussian kernel is utilized as a weighted factor that can be adapted to the global distribution.
Keywords: Computed tomography (CT), image reconstruction, total variation (TV), group sparsity, signal-to-noise ratio (SNR).
