Multi-Frame Super-Resolution Reconstruction Based on Gradient Vector Flow Hybrid Field
In this paper, we propose a novel multi-frame super-resolution (SR) method, which is developed by considering image enhancement and denoising into the SR processing. For image enhancement, a gradient vector flow hybrid field (GVFHF) algorithm, which is robust to noise is first designed to capture th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8052084/ |
| _version_ | 1818457951181996032 |
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| author | Shuying Huang Jun Sun Yong Yang Yuming Fang Pan Lin |
| author_facet | Shuying Huang Jun Sun Yong Yang Yuming Fang Pan Lin |
| author_sort | Shuying Huang |
| collection | DOAJ |
| description | In this paper, we propose a novel multi-frame super-resolution (SR) method, which is developed by considering image enhancement and denoising into the SR processing. For image enhancement, a gradient vector flow hybrid field (GVFHF) algorithm, which is robust to noise is first designed to capture the image edges more accurately. Then, through replacing the gradient of anisotropic diffusion shock filter (ADSF) by GVFHF, a GVFHF-based ADSF (GVFHF-ADSF) model is proposed, which can effectively achieve image denoising and enhancement. In addition, a difference curvature-based spatial weight factor is defined in the GVFHF-ADSF model to obtain an adaptive weight between denoising and enhancement in the flat and edge regions. Finally, a GVFHF-ADSF-based multi-frame SR method is presented by employing the GVFHF-ADSF model as a regularization term and the steepest descent algorithm is adopted to solve the inverse SR problem. Experimental results and comparisons with existing methods demonstrate that the proposed GVFHF-ADSF-based SR algorithm can effectively suppress both Gaussian and salt-and-pepper noise, meanwhile enhance edges of the reconstructed image. |
| first_indexed | 2024-12-14T22:50:43Z |
| format | Article |
| id | doaj.art-0266a336095c4dc985a4291ced4a649c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T22:50:43Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0266a336095c4dc985a4291ced4a649c2022-12-21T22:44:44ZengIEEEIEEE Access2169-35362017-01-015216692168310.1109/ACCESS.2017.27572398052084Multi-Frame Super-Resolution Reconstruction Based on Gradient Vector Flow Hybrid FieldShuying Huang0https://orcid.org/0000-0003-2771-8461Jun Sun1Yong Yang2https://orcid.org/0000-0001-9467-0942Yuming Fang3https://orcid.org/0000-0002-6946-3586Pan Lin4School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang, ChinaSchool of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang, ChinaSchool of Information Technology, Jiangxi University of Finance and Economics, Nanchang, ChinaSchool of Information Technology, Jiangxi University of Finance and Economics, Nanchang, ChinaInstitute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, ChinaIn this paper, we propose a novel multi-frame super-resolution (SR) method, which is developed by considering image enhancement and denoising into the SR processing. For image enhancement, a gradient vector flow hybrid field (GVFHF) algorithm, which is robust to noise is first designed to capture the image edges more accurately. Then, through replacing the gradient of anisotropic diffusion shock filter (ADSF) by GVFHF, a GVFHF-based ADSF (GVFHF-ADSF) model is proposed, which can effectively achieve image denoising and enhancement. In addition, a difference curvature-based spatial weight factor is defined in the GVFHF-ADSF model to obtain an adaptive weight between denoising and enhancement in the flat and edge regions. Finally, a GVFHF-ADSF-based multi-frame SR method is presented by employing the GVFHF-ADSF model as a regularization term and the steepest descent algorithm is adopted to solve the inverse SR problem. Experimental results and comparisons with existing methods demonstrate that the proposed GVFHF-ADSF-based SR algorithm can effectively suppress both Gaussian and salt-and-pepper noise, meanwhile enhance edges of the reconstructed image.https://ieeexplore.ieee.org/document/8052084/Super-resolutiongradient vector flowshock filterimage enhancementregularization |
| spellingShingle | Shuying Huang Jun Sun Yong Yang Yuming Fang Pan Lin Multi-Frame Super-Resolution Reconstruction Based on Gradient Vector Flow Hybrid Field IEEE Access Super-resolution gradient vector flow shock filter image enhancement regularization |
| title | Multi-Frame Super-Resolution Reconstruction Based on Gradient Vector Flow Hybrid Field |
| title_full | Multi-Frame Super-Resolution Reconstruction Based on Gradient Vector Flow Hybrid Field |
| title_fullStr | Multi-Frame Super-Resolution Reconstruction Based on Gradient Vector Flow Hybrid Field |
| title_full_unstemmed | Multi-Frame Super-Resolution Reconstruction Based on Gradient Vector Flow Hybrid Field |
| title_short | Multi-Frame Super-Resolution Reconstruction Based on Gradient Vector Flow Hybrid Field |
| title_sort | multi frame super resolution reconstruction based on gradient vector flow hybrid field |
| topic | Super-resolution gradient vector flow shock filter image enhancement regularization |
| url | https://ieeexplore.ieee.org/document/8052084/ |
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