Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters
The primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approa...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/8309910 |
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| author | Mahmoud M. Khattab Akram M. Zeki Ali A. Alwan Belgacem Bouallegue Safaa S. Matter Abdelmoty M. Ahmed |
| author_facet | Mahmoud M. Khattab Akram M. Zeki Ali A. Alwan Belgacem Bouallegue Safaa S. Matter Abdelmoty M. Ahmed |
| author_sort | Mahmoud M. Khattab |
| collection | DOAJ |
| description | The primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approaches are typically affected by annoying restorative artifacts, including blurring, noise, and staircasing effect. Accordingly, it is always difficult to balance between smoothness and edge preservation. In this paper, we intend to enhance the efficiency of multiframe super-resolution image reconstruction in order to optimize both analysis and human interpretation processes by improving the pictorial information and enhancing the automatic machine perception. As a result, we propose new approaches that firstly rely on estimating the initial high-resolution image through preprocessing of the reference low-resolution image based on median, mean, Lucy-Richardson, and Wiener filters. This preprocessing stage is used to overcome the degradation present in the reference low-resolution image, which is a suitable kernel for producing the initial high-resolution image to be used in the reconstruction phase of the final image. Then, L2 norm is employed for the data-fidelity term to minimize the residual among the predicted high-resolution image and the observed low-resolution images. Finally, bilateral total variation prior model is utilized to restrict the minimization function to a stable state of the generated HR image. The experimental results of the synthetic data indicate that the proposed approaches have enhanced efficiency visually and quantitatively compared to other existing approaches. |
| format | Article |
| id | doaj-art-9f302d9d06844cf0a7c50c951554eb6d |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-9f302d9d06844cf0a7c50c951554eb6d2025-02-03T07:24:15ZengWileyJournal of Electrical and Computer Engineering2090-01552021-01-01202110.1155/2021/8309910Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear FiltersMahmoud M. Khattab0Akram M. Zeki1Ali A. Alwan2Belgacem Bouallegue3Safaa S. Matter4Abdelmoty M. Ahmed5Faculty of Information and Communication TechnologyFaculty of Information and Communication TechnologySchool of Theoretical & Applied ScienceCollege of Computer ScienceCommunity CollegeCollege of Computer ScienceThe primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approaches are typically affected by annoying restorative artifacts, including blurring, noise, and staircasing effect. Accordingly, it is always difficult to balance between smoothness and edge preservation. In this paper, we intend to enhance the efficiency of multiframe super-resolution image reconstruction in order to optimize both analysis and human interpretation processes by improving the pictorial information and enhancing the automatic machine perception. As a result, we propose new approaches that firstly rely on estimating the initial high-resolution image through preprocessing of the reference low-resolution image based on median, mean, Lucy-Richardson, and Wiener filters. This preprocessing stage is used to overcome the degradation present in the reference low-resolution image, which is a suitable kernel for producing the initial high-resolution image to be used in the reconstruction phase of the final image. Then, L2 norm is employed for the data-fidelity term to minimize the residual among the predicted high-resolution image and the observed low-resolution images. Finally, bilateral total variation prior model is utilized to restrict the minimization function to a stable state of the generated HR image. The experimental results of the synthetic data indicate that the proposed approaches have enhanced efficiency visually and quantitatively compared to other existing approaches.http://dx.doi.org/10.1155/2021/8309910 |
| spellingShingle | Mahmoud M. Khattab Akram M. Zeki Ali A. Alwan Belgacem Bouallegue Safaa S. Matter Abdelmoty M. Ahmed Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters Journal of Electrical and Computer Engineering |
| title | Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters |
| title_full | Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters |
| title_fullStr | Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters |
| title_full_unstemmed | Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters |
| title_short | Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters |
| title_sort | regularized multiframe super resolution image reconstruction using linear and nonlinear filters |
| url | http://dx.doi.org/10.1155/2021/8309910 |
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