Improved TV Image Denoising over Inverse Gradient
Noise in an image can affect one’s extraction of image information, therefore, image denoising is an important image pre-processing process. Many of the existing models have a large number of estimated parameters, which increases the time complexity of the model solution and the achieved denoising e...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/15/3/678 |
| _version_ | 1797608879617474560 |
|---|---|
| author | Minmin Li Guangcheng Cai Shaojiu Bi Xi Zhang |
| author_facet | Minmin Li Guangcheng Cai Shaojiu Bi Xi Zhang |
| author_sort | Minmin Li |
| collection | DOAJ |
| description | Noise in an image can affect one’s extraction of image information, therefore, image denoising is an important image pre-processing process. Many of the existing models have a large number of estimated parameters, which increases the time complexity of the model solution and the achieved denoising effect is less than ideal. As a result, in this paper, an improved image-denoising algorithm is proposed based on the TV model, which effectively solves the above problems. The L<sub>1</sub> regularization term can make the solution generated by the model sparser, thus facilitating the recovery of high-quality images. Reducing the number of estimated parameters, while using the inverse gradient to estimate the regularization parameters, enables the parameters to achieve global adaption and improves the denoising effect of the model in combination with the TV regularization term. The split Bregman iteration method is used to decouple the model into several related subproblems, and the solutions of the coordinated subproblems are derived as optimal solutions. It is also shown that the solution of the model converges to a Karush–Kuhn–Tucker point. Experimental results show that the algorithm in this paper is more effective in both preserving image texture structure and suppressing image noise. |
| first_indexed | 2024-03-11T05:50:44Z |
| format | Article |
| id | doaj.art-eba0337f00e54e0d8efce3b844caffee |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-11T05:50:44Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-eba0337f00e54e0d8efce3b844caffee2023-11-17T14:09:27ZengMDPI AGSymmetry2073-89942023-03-0115367810.3390/sym15030678Improved TV Image Denoising over Inverse GradientMinmin Li0Guangcheng Cai1Shaojiu Bi2Xi Zhang3Faculty of Science, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Science, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Science, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Science, Kunming University of Science and Technology, Kunming 650500, ChinaNoise in an image can affect one’s extraction of image information, therefore, image denoising is an important image pre-processing process. Many of the existing models have a large number of estimated parameters, which increases the time complexity of the model solution and the achieved denoising effect is less than ideal. As a result, in this paper, an improved image-denoising algorithm is proposed based on the TV model, which effectively solves the above problems. The L<sub>1</sub> regularization term can make the solution generated by the model sparser, thus facilitating the recovery of high-quality images. Reducing the number of estimated parameters, while using the inverse gradient to estimate the regularization parameters, enables the parameters to achieve global adaption and improves the denoising effect of the model in combination with the TV regularization term. The split Bregman iteration method is used to decouple the model into several related subproblems, and the solutions of the coordinated subproblems are derived as optimal solutions. It is also shown that the solution of the model converges to a Karush–Kuhn–Tucker point. Experimental results show that the algorithm in this paper is more effective in both preserving image texture structure and suppressing image noise.https://www.mdpi.com/2073-8994/15/3/678image denoisinginverse gradientregularization parametersplit Bregman iterative methodKarush–Kuhn–Tucker condition |
| spellingShingle | Minmin Li Guangcheng Cai Shaojiu Bi Xi Zhang Improved TV Image Denoising over Inverse Gradient Symmetry image denoising inverse gradient regularization parameter split Bregman iterative method Karush–Kuhn–Tucker condition |
| title | Improved TV Image Denoising over Inverse Gradient |
| title_full | Improved TV Image Denoising over Inverse Gradient |
| title_fullStr | Improved TV Image Denoising over Inverse Gradient |
| title_full_unstemmed | Improved TV Image Denoising over Inverse Gradient |
| title_short | Improved TV Image Denoising over Inverse Gradient |
| title_sort | improved tv image denoising over inverse gradient |
| topic | image denoising inverse gradient regularization parameter split Bregman iterative method Karush–Kuhn–Tucker condition |
| url | https://www.mdpi.com/2073-8994/15/3/678 |
| work_keys_str_mv | AT minminli improvedtvimagedenoisingoverinversegradient AT guangchengcai improvedtvimagedenoisingoverinversegradient AT shaojiubi improvedtvimagedenoisingoverinversegradient AT xizhang improvedtvimagedenoisingoverinversegradient |