Introducing anisotropic tensor to high order variational model for image restoration
Second order total variation (SOTV) models have advantages for image restoration over their first order counterparts including their ability to remove the staircase artefact in the restored image. However, such models tend to blur the reconstructed image when discretised for numerical solution [1–5]...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Elsevier
2017
|
| Online Access: | https://eprints.nottingham.ac.uk/44633/ |
| _version_ | 1848796961455472640 |
|---|---|
| author | Duan, Jinming Ward, Wil O.C. Sibbett, Luke Pan, Zhenkuan Bai, Li |
| author_facet | Duan, Jinming Ward, Wil O.C. Sibbett, Luke Pan, Zhenkuan Bai, Li |
| author_sort | Duan, Jinming |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Second order total variation (SOTV) models have advantages for image restoration over their first order counterparts including their ability to remove the staircase artefact in the restored image. However, such models tend to blur the reconstructed image when discretised for numerical solution [1–5]. To overcome this drawback, we introduce a new tensor weighted second order (TWSO) model for image restoration. Specifically, we develop a novel regulariser for the SOTV model that uses the Frobenius norm of the product of the isotropic SOTV Hessian matrix and an anisotropic tensor. We then adapt the alternating direction method of multipliers (ADMM) to solve the proposed model by breaking down the original problem into several subproblems. All the subproblems have closed-forms and can be solved efficiently. The proposed method is compared with state-of-the-art approaches such as tensor-based anisotropic diffusion, total generalised variation, and Euler's elastica. We validate the proposed TWSO model using extensive experimental results on a large number of images from the Berkeley BSDS500. We also demonstrate that our method effectively reduces both the staircase and blurring effects and outperforms existing approaches for image inpainting and denoising applications. |
| first_indexed | 2025-11-14T19:56:18Z |
| format | Article |
| id | nottingham-44633 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:56:18Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-446332020-05-04T18:55:14Z https://eprints.nottingham.ac.uk/44633/ Introducing anisotropic tensor to high order variational model for image restoration Duan, Jinming Ward, Wil O.C. Sibbett, Luke Pan, Zhenkuan Bai, Li Second order total variation (SOTV) models have advantages for image restoration over their first order counterparts including their ability to remove the staircase artefact in the restored image. However, such models tend to blur the reconstructed image when discretised for numerical solution [1–5]. To overcome this drawback, we introduce a new tensor weighted second order (TWSO) model for image restoration. Specifically, we develop a novel regulariser for the SOTV model that uses the Frobenius norm of the product of the isotropic SOTV Hessian matrix and an anisotropic tensor. We then adapt the alternating direction method of multipliers (ADMM) to solve the proposed model by breaking down the original problem into several subproblems. All the subproblems have closed-forms and can be solved efficiently. The proposed method is compared with state-of-the-art approaches such as tensor-based anisotropic diffusion, total generalised variation, and Euler's elastica. We validate the proposed TWSO model using extensive experimental results on a large number of images from the Berkeley BSDS500. We also demonstrate that our method effectively reduces both the staircase and blurring effects and outperforms existing approaches for image inpainting and denoising applications. Elsevier 2017-07-12 Article PeerReviewed Duan, Jinming, Ward, Wil O.C., Sibbett, Luke, Pan, Zhenkuan and Bai, Li (2017) Introducing anisotropic tensor to high order variational model for image restoration. Digital Signal Processing . ISSN 1051-2004 http://www.sciencedirect.com/science/article/pii/S1051200417301434 doi:10.1016/j.dsp.2017.07.001 doi:10.1016/j.dsp.2017.07.001 |
| spellingShingle | Duan, Jinming Ward, Wil O.C. Sibbett, Luke Pan, Zhenkuan Bai, Li Introducing anisotropic tensor to high order variational model for image restoration |
| title | Introducing anisotropic tensor to high order variational model for image restoration |
| title_full | Introducing anisotropic tensor to high order variational model for image restoration |
| title_fullStr | Introducing anisotropic tensor to high order variational model for image restoration |
| title_full_unstemmed | Introducing anisotropic tensor to high order variational model for image restoration |
| title_short | Introducing anisotropic tensor to high order variational model for image restoration |
| title_sort | introducing anisotropic tensor to high order variational model for image restoration |
| url | https://eprints.nottingham.ac.uk/44633/ https://eprints.nottingham.ac.uk/44633/ https://eprints.nottingham.ac.uk/44633/ |