Spatially adaptive local-feature-driven total variation minimizing image restoration

1997 ◽  
Author(s):  
David M. Strong ◽  
Peter Blomgren ◽  
Tony F. Chan
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Local Feature ◽  
Spatially Adaptive

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.

2D Wavelet Tree Ordering Based Localized Total Variation Model for Efficient Image Restoration

2022 ◽  
Author(s):  
K. Praveen Kumar ◽  
C. Venkata Narasimhulu ◽  
K. Satya Prasad
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Good Condition ◽  
Window Size ◽  
Local Variation ◽  
Image Simulation ◽  
Regularization Algorithm ◽  
Total Variation Model ◽  
Degraded Image ◽  
Adjustment Scheme

The degraded image during the process of image analysis needs more number of iterations to restore it. These iterations take long waiting time and slow scanning, resulting in inefficient image restoration. A few numbers of measurements are enough to recuperate an image with good condition. Due to tree sparsity, a 2D wavelet tree reduces the number of coefficients and iterations to restore the degraded image. All the wavelet coefficients are extracted with overlaps as low and high sub-band space and ordered them such that they are decomposed in the tree ordering structured path. Some articles have addressed the problems with tree sparsity and total variation (TV), but few authors endorsed the benefits of tree sparsity. In this paper, a spatial variation regularization algorithm based on tree order is implemented to change the window size and variation estimators to reduce the loss of image information and to solve the problem of image smoothing operation. The acceptance rate of the tree-structured path relies on local variation estimators to regularize the performance parameters and update them to restore the image. For this, the Localized Total Variation (LTV) method is proposed and implemented on a 2D wavelet tree ordering structured path based on the proposed image smooth adjustment scheme. In the end, a reliable reordering algorithm proposed to reorder the set of pixels and to increase the reliability of the restored image. Simulation results clearly show that the proposed method improved the performance compared to existing methods of image restoration.

Wavelet-based Total Variation Equation Image Restoration

2013 ◽  
Vol 12 (23) ◽  
pp. 7778-7781
Author(s):  
Zhao Dong-Hong ◽  
Wang Chen-Chen
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Variation Equation
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