scholarly journals Non-Blind Image Deblurring Method Using Shear High Order Total Variation Norm

2021 ◽  
Vol 26 (6) ◽  
pp. 495-506
Author(s):  
Lixuan LU ◽  
Tao ZHANG
Keyword(s):  
Total Variation ◽  
High Order ◽  
Perceptual Quality ◽  
Total Variation Norm ◽  
Edge Information ◽  
Blind Image Deblurring ◽  
Alternating Direction ◽  
Blind Deblurring ◽  
Proposed Model ◽  
Blind Image

In this paper, we propose a shear high-order gradient (SHOG) operator by combining the shear operator and high-order gradient (HOG) operator. Compared with the HOG operator, the proposed SHOG operator can incorporate more directionality and detect more abundant edge information. Based on the SHOG operator, we extend the total variation (TV) norm to shear high-order total variation (SHOTV), and then propose a SHOTV deblurring model. We also study some properties of the SHOG operator, and show that the SHOG matrices are Block Circulant with Circulant Blocks (BCCB) when the shear angle is [see formula in PDF]. The proposed model is solved efficiently by the alternating direction method of multipliers (ADMM). Experimental results demonstrate that the proposed method outperforms some state-of-the-art non-blind deblurring methods in both objective and perceptual quality.

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.

scholarly journals Blind Deblurring Based on Sigmoid Function

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3484
Author(s):  
Shuhan Sun ◽  
Lizhen Duan ◽  
Zhiyong Xu ◽  
Jianlin Zhang
Keyword(s):  
Performance Optimization ◽  
Degradation Process ◽  
Image Deconvolution ◽  
Sigmoid Function ◽  
Blind Image Deblurring ◽  
Linear Estimators ◽  
Blind Deblurring ◽  
Degraded Image ◽  
Excellent Property ◽  
Blind Image

Blind image deblurring, also known as blind image deconvolution, is a long-standing challenge in the field of image processing and low-level vision. To restore a clear version of a severely degraded image, this paper proposes a blind deblurring algorithm based on the sigmoid function, which constructs novel blind deblurring estimators for both the original image and the degradation process by exploring the excellent property of sigmoid function and considering image derivative constraints. Owing to these symmetric and non-linear estimators of low computation complexity, high-quality images can be obtained by the algorithm. The algorithm is also extended to image sequences. The sigmoid function enables the proposed algorithm to achieve state-of-the-art performance in various scenarios, including natural, text, face, and low-illumination images. Furthermore, the method can be extended naturally to non-uniform deblurring. Quantitative and qualitative experimental evaluations indicate that the algorithm can remove the blur effect and improve the image quality of actual and simulated images. Finally, the use of sigmoid function provides a new approach to algorithm performance optimization in the field of image restoration.

scholarly journals Blind Image Deblurring Based on Local Edges Selection

2019 ◽  
Vol 9 (16) ◽  
pp. 3274
Author(s):  
Han ◽  
Kan
Keyword(s):  
Image Deblurring ◽  
Color Images ◽  
Clear Image ◽  
Blind Image Deblurring ◽  
Alternating Direction ◽  
Dark Channel ◽  
The Difference ◽  
And Performance ◽  
Image Edges ◽  
Blind Image

The edges of images are less sparse when images become blurred. Selecting effective image edges is a vital step in image deblurring, which can help us to build image deblurring models more accurately. While global edges selection methods tend to fail in capturing dense image structures, the edges are easy to be affected by noise and blur. In this paper, we propose an image deblurring method based on local edges selection. The local edges are selected by the difference between the bright channel and the dark channel. Then a novel image deblurring model including local edges regularization term is established. The obtaining of a clear image and blurring kernel is based on alternating iterations, in which the clear image is obtained by the alternating direction method of multipliers (ADMM). In the experiments, tests are carried out on gray value images, synthetic color images and natural color images. Compared with other state-of-the-art blind image deblurring methods, the visualization results and performance verify the effectiveness of our method.

scholarly journals Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 174 ◽  
Author(s):  
Naixue Xiong ◽  
Ryan Liu ◽  
Maohan Liang ◽  
Di Wu ◽  
Zhao Liu ◽  
...  
Keyword(s):  
Optimization Methods ◽  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Alternating Direction ◽  
Blind Image

scholarly journals Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage

2020 ◽  
Vol 10 (7) ◽  
pp. 2533 ◽  
Author(s):  
Jingjing Yang ◽  
Yingpin Chen ◽  
Zhifeng Chen
Keyword(s):  
Total Variation ◽  
Infrared Image ◽  
Noise Pollution ◽  
Image Deblurring ◽  
High Order ◽  
L1 Norm ◽  
Alternating Direction ◽  
Image Blurring ◽  
Image Gradients ◽  
Staircase Artifacts

The quality of infrared images is affected by various degradation factors, such as image blurring and noise pollution. Anisotropic total variation (ATV) has been shown to be a good regularization approach for image deblurring. However, there are two main drawbacks in ATV. First, the conventional ATV regularization just considers the sparsity of the first-order image gradients, thus leading to staircase artifacts. Second, it employs the L1-norm to describe the sparsity of image gradients, while the L1-norm has a limited capacity of depicting the sparsity of sparse variables. To address these limitations of ATV, a high-order total variation is introduced in the ATV deblurring model and the Lp-pseudonorm is adopted to depict the sparsity of low- and high-order total variation. In this way, the recovered image can fit the image priors with clear edges and eliminate the staircase artifacts of the ATV model. The alternating direction method of multipliers is used to solve the proposed model. The experimental results demonstrate that the proposed method does not only remove blurs effectively but is also highly competitive against the state-of-the-art methods, both qualitatively and quantitatively.

scholarly journals Spectral Norm Regularization for Blind Image Deblurring

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1856
Author(s):  
Shuhan Sun ◽  
Zhiyong Xu ◽  
Jianlin Zhang
Keyword(s):  
Degradation Process ◽  
Image Deblurring ◽  
Spectral Norm ◽  
Blind Image Deblurring ◽  
Blind Deblurring ◽  
Image Regularization ◽  
Symmetrical Form ◽  
Ill Posed ◽  
Degraded Images ◽  
Blind Image

Blind image deblurring is a well-known ill-posed inverse problem in the computer vision field. To make the problem well-posed, this paper puts forward a plain but effective regularization method, namely spectral norm regularization (SN), which can be regarded as the symmetrical form of the spectral norm. This work is inspired by the observation that the SN value increases after the image is blurred. Based on this observation, a blind deblurring algorithm (BDA-SN) is designed. BDA-SN builds a deblurring estimator for the image degradation process by investigating the inherent properties of SN and an image gradient. Compared with previous image regularization methods, SN shows more vital abilities to differentiate clear and degraded images. Therefore, the SN of an image can effectively help image deblurring in various scenes, such as text, face, natural, and saturated images. Qualitative and quantitative experimental evaluations demonstrate that BDA-SN can achieve favorable performances on actual and simulated images, with the average PSNR reaching 31.41, especially on the benchmark dataset of Levin et al.

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