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.

An Enhanced NAS-RIF Algorithm for Blind Image Restoration Based on Total Variation Regularization

2013 ◽  
Vol 423-426 ◽  
pp. 2522-2525
Author(s):  
Xin Ke Li ◽  
Chao Gao ◽  
Yong Cai Guo ◽  
Yan Hua Shao
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Tikhonov Regularization Method ◽  
Total Variation Regularization ◽  
Inverse Filtering ◽  
Blind Image Restoration ◽  
Spatially Adaptive ◽  
Blind Image

In order to improve the quality of blind image restoration, we propose an algorithm which combines Non-negativity and Support constraint Recursive Inverse Filtering (NAS-RIF) and adaptive total variation regularization. In the proposed algorithm, the total variation regularization constraint term is added in the NAS-RIF algorithm cost function. The majorization-minimization approach and conjugate gradient iterative algorithm are adopted to improve the convergence speed. We do the simulation experiments for the blurred classic test image which is added additive random noise. Experimental results show that the restoration effect of our algorithm is better than the spatially adaptive Tikhonov regularization method and the NAS-RIF spatially adaptive regularization algorithm, while the value of improvement of signal to noise ratio (ISNR) has improved.

scholarly journals An Efficient Variational Method for Image Restoration

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Liu ◽  
Ting-Zhu Huang ◽  
Xiao-Guang Lv ◽  
Si Wang
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Total Variation Regularization ◽  
Split Bregman ◽  
Split Bregman Iteration ◽  
Imaging Science ◽  
Constrained Minimization Problem ◽  
Structure Similarity Index

Image restoration is one of the most fundamental issues in imaging science. Total variation regularization is widely used in image restoration problems for its capability to preserve edges. In this paper, we consider a constrained minimization problem with double total variation regularization terms. To solve this problem, we employ the split Bregman iteration method and the Chambolle’s algorithm. The convergence property of the algorithm is established. The numerical results demonstrate the effectiveness of the proposed method in terms of peak signal-to-noise ratio (PSNR) and the structure similarity index (SSIM).

scholarly journals Image Restoration by a Mixed High-Order Total Variation and l1 Regularization Model

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jianguang Zhu ◽  
Kai Li ◽  
Binbin Hao
Keyword(s):  
Total Variation ◽  
High Order ◽  
Variational Model ◽  
Total Variation Regularization ◽  
Current State ◽  
Model Combining ◽  
Alternating Direction ◽  
Proposed Model ◽  
Sharp Edges ◽  
Thresholding Algorithm

Total variation regularization is well-known for recovering sharp edges; however, it usually produces staircase artifacts. In this paper, in order to overcome the shortcoming of total variation regularization, we propose a new variational model combining high-order total variation regularization and l1 regularization. The new model has separable structure which enables us to solve the involved subproblems more efficiently. We propose a fast alternating method by employing the fast iterative shrinkage-thresholding algorithm (FISTA) and the alternating direction method of multipliers (ADMM). Compared with some current state-of-the-art methods, numerical experiments show that our proposed model can significantly improve the quality of restored images and obtain higher SNR and SSIM values.

A compensating total variation image denoising model combining L1 and L2 norm

2020 ◽  
pp. 002072092092330
Author(s):  
Liqiong Zhang ◽  
Min Li ◽  
Xiaohua Qiu
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Isotropic Diffusion ◽  
Model Combining ◽  
L2 Norm ◽  
Staircase Effect

To overcome the “staircase effect” while preserving the structural information such as image edges and textures quickly and effectively, we propose a compensating total variation image denoising model combining L1 and L2 norm. A new compensating regular term is designed, which can perform anisotropic and isotropic diffusion in image denoising, thus making up for insufficient diffusion in the total variation model. The algorithm first uses local standard deviation to distinguish neighborhood types. Then, the anisotropic diffusion based on L1 norm plays the role of edge protection in the strong edge region. The anisotropic and the isotropic diffusion simultaneously exist in the smooth region, so that the weak textures can be protected while overcoming the “staircase effect” effectively. The simulation experiments show that this method can effectively improve the peak signal-to-noise ratio and obtain the higher structural similarity index and the shorter running time.

scholarly journals Construction model for total variation regularization parameter

2014 ◽  
Vol 22 (9) ◽  
pp. 10500 ◽  
Author(s):  
Guanghua Gong ◽  
Hongming Zhang ◽  
Minyu Yao
Keyword(s):  
Total Variation ◽  
Regularization Parameter ◽  
Total Variation Regularization ◽  
Construction Model
Sign in / Sign up

Export Citation Format

Share Document