New image restoration method associated with tetrolets shrinkage and weighted anisotropic total variation

2013 ◽  
Vol 93 (4) ◽  
pp. 661-670 ◽  
Author(s):  
Liqian Wang ◽  
Liang Xiao ◽  
Jun Zhang ◽  
Zhihui Wei
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Restoration Method ◽  
Anisotropic Total Variation

scholarly journals Total Variation Image Restoration Method Based on Subspace Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
XiaoGuang Liu ◽  
XingBao Gao
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Energy Function ◽  
Alternating Direction Method ◽  
Numerical Examples ◽  
Subspace Optimization ◽  
Alternating Direction ◽  
Restoration Method ◽  
Weaker Conditions ◽  
Search Directions

The alternating direction method is widely applied in total variation image restoration. However, the search directions of the method are not accurate enough. In this paper, one method based on the subspace optimization is proposed to improve its optimization performance. This method corrects the search directions of primal alternating direction method by using the energy function and a linear combination of the previous search directions. In addition, the convergence of the primal alternating direction method is proven under some weaker conditions. Thus the convergence of the corrected method could be easily obtained since it has same convergence with the primal alternating direction method. Numerical examples are given to show the performance of proposed method finally.

scholarly journals A Novel Image-Restoration Method Based on High-Order Total Variation Regularization Term

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 867 ◽  
Author(s):  
Jianhong Xiang ◽  
Pengfei Ye ◽  
Linyu Wang ◽  
Mingqi He
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Regularization Parameter ◽  
Signal To Noise Ratio ◽  
High Order ◽  
Computational Time ◽  
Total Variation Regularization ◽  
Selection Scheme ◽  
Staircase Effect ◽  
Restoration Method

This paper presents two new models for solving image the deblurring problem in the presence of impulse noise. One involves a high-order total variation (TV) regularizer term in the corrected total variation L1 (CTVL1) model and is named high-order corrected TVL1 (HOCTVL1). This new model can not only suppress the defects of the staircase effect, but also improve the quality of image restoration. In most cases, the regularization parameter in the model is a fixed value, which may influence processing results. Aiming at this problem, the spatially adapted regularization parameter selection scheme is involved in HOCTVL1 model, and spatially adapted HOCTVL1 (SAHOCTVL1) model is proposed. When dealing with corrupted images, the regularization parameter in SAHOCTVL1 model can be updated automatically. Many numerical experiments are conducted in this paper and the results show that the two models can significantly improve the effects both in visual quality and signal-to-noise ratio (SNR) at the expense of a small increase in computational time. Compared to HOCTVL1 model, SAHOCTVL1 model can restore more texture details, though it may take more time.

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