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).

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.

Removal of Glass Reflection Using TV-Retinex Model

2014 ◽  
Vol 511-512 ◽  
pp. 475-480
Author(s):  
Ke Fei Cheng ◽  
Dan Ni Li
Keyword(s):  
Total Variation ◽  
Computational Approach ◽  
Total Variation Regularization ◽  
Bregman Iteration ◽  
Split Bregman ◽  
Split Bregman Iteration

The Retinex model is mainly used to removal of unfavorable illumination effects from images. In this paper, the Retinex model combined with the total variation regularization (TV-Retinex) is presented to removal of glass reflection that can be solved by a fast computational approach based on the split Bregman iteration. Experiments demonstrated that the proposed method can effectively reduce this kind of artifact as well as preserve the edge and detailed information.

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.

scholarly journals A Unidirectional Total Variation and Second-Order Total Variation Model for Destriping of Remote Sensing Images

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Min Wang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Liang-Jian Deng ◽  
Gang Liu
Keyword(s):  
Remote Sensing ◽  
Total Variation ◽  
Second Order ◽  
Total Variation Regularization ◽  
Remote Sensing Images ◽  
Split Bregman ◽  
Directional Characteristic ◽  
Directional Information ◽  
Split Bregman Iteration ◽  
Large Width

Remote sensing images often suffer from stripe noise, which greatly degrades the image quality. Destriping of remote sensing images is to recover a good image from the image containing stripe noise. Since the stripes in remote sensing images have a directional characteristic (horizontal or vertical), the unidirectional total variation has been used to consider the directional information and preserve the edges. The remote sensing image contaminated by heavy stripe noise always has large width stripes and the pixels in the stripes have low correlations with the true pixels. On this occasion, the destriping process can be viewed as inpainting the wide stripe domains. In many works, high-order total variation has been proved to be a powerful tool to inpainting wide domains. Therefore, in this paper, we propose a variational destriping model that combines unidirectional total variation and second-order total variation regularization to employ the directional information and handle the wide stripes. In particular, the split Bregman iteration method is employed to solve the proposed model. Experimental results demonstrate the effectiveness of the proposed method.

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