Image denoising using combined higher order non-convex total variation with overlapping group sparsity

2018 ◽  
Vol 30 (1) ◽  
pp. 503-527 ◽  
Author(s):  
Tarmizi Adam ◽  
Raveendran Paramesran
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Higher Order ◽  
Group Sparsity ◽  
Overlapping Group Sparsity

Total variation with overlapping group sparsity for speckle noise reduction

2016 ◽  
Vol 216 ◽  
pp. 502-513 ◽  
Author(s):  
Jun Liu ◽  
Ting-Zhu Huang ◽  
Gang Liu ◽  
Si Wang ◽  
Xiao-Guang Lv
Keyword(s):  
Noise Reduction ◽  
Total Variation ◽  
Speckle Noise ◽  
Group Sparsity ◽  
Overlapping Group Sparsity ◽  
Speckle Noise Reduction

Total variation with overlapping group sparsity for deblurring images under Cauchy noise

2019 ◽  
Vol 341 ◽  
pp. 128-147 ◽  
Author(s):  
Meng Ding ◽  
Ting-Zhu Huang ◽  
Si Wang ◽  
Jin-Jin Mei ◽  
Xi-Le Zhao
Keyword(s):  
Total Variation ◽  
Group Sparsity ◽  
Overlapping Group Sparsity

Total variation with modified group sparsity for CT reconstruction under low SNR

2021 ◽  
pp. 1-18
Author(s):  
Lingli Zhang
Keyword(s):  
Total Variation ◽  
Signal To Noise Ratio ◽  
Gaussian Kernel ◽  
Reconstruction Technique ◽  
Ct Reconstruction ◽  
Group Sparsity ◽  
Low Snr ◽  
Overlapping Group Sparsity ◽  
Weighted Factor ◽  
Tv Model

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.

Sign in / Sign up

Export Citation Format

Share Document