A new low‐complexity patch‐based image super‐resolution
In this study, a novel single image super‐resolution (SR) method, which uses a generated dictionary from pairs of high‐resolution (HR) images and their corresponding low‐resolution (LR) representations, is proposed. First, HR and LR dictionaries are created by dividing HR and LR images into patches...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-10-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2016.0463 |
| _version_ | 1797684931977019392 |
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| author | Pejman Rasti Kamal Nasrollahi Olga Orlova Gert Tamberg Cagri Ozcinar Thomas B. Moeslund Gholamreza Anbarjafari |
| author_facet | Pejman Rasti Kamal Nasrollahi Olga Orlova Gert Tamberg Cagri Ozcinar Thomas B. Moeslund Gholamreza Anbarjafari |
| author_sort | Pejman Rasti |
| collection | DOAJ |
| description | In this study, a novel single image super‐resolution (SR) method, which uses a generated dictionary from pairs of high‐resolution (HR) images and their corresponding low‐resolution (LR) representations, is proposed. First, HR and LR dictionaries are created by dividing HR and LR images into patches Afterwards, when performing SR, the distance between every patch of the input LR image and those of available LR patches in the LR dictionary are calculated. The minimum distance between the input LR patch and those in the LR dictionary is taken, and its counterpart from the HR dictionary will be passed through an illumination enhancement process resulting in consistency of illumination between neighbour patches. This process is applied to all patches of the LR image. Finally, in order to remove the blocking effect caused by merging the patches, an average of the obtained HR image and the interpolated image is calculated. Furthermore, it is shown that the stabe of dictionaries is reducible to a great degree. The speed of the system is improved by 62.5%. The quantitative and qualitative analyses of the experimental results show the superiority of the proposed technique over the conventional and state‐of‐the‐art methods. |
| first_indexed | 2024-03-12T00:36:57Z |
| format | Article |
| id | doaj.art-2b9677eb2fe14710847cac4946dd2db0 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:36:57Z |
| publishDate | 2017-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-2b9677eb2fe14710847cac4946dd2db02023-09-15T09:32:59ZengWileyIET Computer Vision1751-96321751-96402017-10-0111756757610.1049/iet-cvi.2016.0463A new low‐complexity patch‐based image super‐resolutionPejman Rasti0Kamal Nasrollahi1Olga Orlova2Gert Tamberg3Cagri Ozcinar4Thomas B. Moeslund5Gholamreza Anbarjafari6iCV Research Group, Institute of Technology, University of TartuDepartment of Cybernetics, TartuEstoniaVisual Analysis of People LaboratoryAalborg UniversityAalborgDenmarkTallinn University of TechnologyTallinnEstoniaTallinn University of TechnologyTallinnEstoniaSchool of Computer Science and StatisticsTrinity College DublinDublin2IrelandVisual Analysis of People LaboratoryAalborg UniversityAalborgDenmarkiCV Research Group, Institute of Technology, University of TartuDepartment of Cybernetics, TartuEstoniaIn this study, a novel single image super‐resolution (SR) method, which uses a generated dictionary from pairs of high‐resolution (HR) images and their corresponding low‐resolution (LR) representations, is proposed. First, HR and LR dictionaries are created by dividing HR and LR images into patches Afterwards, when performing SR, the distance between every patch of the input LR image and those of available LR patches in the LR dictionary are calculated. The minimum distance between the input LR patch and those in the LR dictionary is taken, and its counterpart from the HR dictionary will be passed through an illumination enhancement process resulting in consistency of illumination between neighbour patches. This process is applied to all patches of the LR image. Finally, in order to remove the blocking effect caused by merging the patches, an average of the obtained HR image and the interpolated image is calculated. Furthermore, it is shown that the stabe of dictionaries is reducible to a great degree. The speed of the system is improved by 62.5%. The quantitative and qualitative analyses of the experimental results show the superiority of the proposed technique over the conventional and state‐of‐the‐art methods.https://doi.org/10.1049/iet-cvi.2016.0463low-complexity patchsingle image super-resolution methodSR methodhigh-resolution imagesHR imagesLR images |
| spellingShingle | Pejman Rasti Kamal Nasrollahi Olga Orlova Gert Tamberg Cagri Ozcinar Thomas B. Moeslund Gholamreza Anbarjafari A new low‐complexity patch‐based image super‐resolution IET Computer Vision low-complexity patch single image super-resolution method SR method high-resolution images HR images LR images |
| title | A new low‐complexity patch‐based image super‐resolution |
| title_full | A new low‐complexity patch‐based image super‐resolution |
| title_fullStr | A new low‐complexity patch‐based image super‐resolution |
| title_full_unstemmed | A new low‐complexity patch‐based image super‐resolution |
| title_short | A new low‐complexity patch‐based image super‐resolution |
| title_sort | new low complexity patch based image super resolution |
| topic | low-complexity patch single image super-resolution method SR method high-resolution images HR images LR images |
| url | https://doi.org/10.1049/iet-cvi.2016.0463 |
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