Adaptive regularised l2‐boosting on clustered sparse coefficients for single image super‐resolution
In this study, the authors propose a novel approach for single image super‐resolution. Their method is based on the idea of learning a mapping function, which can reveal the intrinsic relationship between sparse coefficients of low‐resolution (LR) and high‐resolution (HR) image patch pairs with resp...
| 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.2016.0274 |
| _version_ | 1797684944763355136 |
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| author | Yulan Han Yongping Zhao Haifeng Yu |
| author_facet | Yulan Han Yongping Zhao Haifeng Yu |
| author_sort | Yulan Han |
| collection | DOAJ |
| description | In this study, the authors propose a novel approach for single image super‐resolution. Their method is based on the idea of learning a mapping function, which can reveal the intrinsic relationship between sparse coefficients of low‐resolution (LR) and high‐resolution (HR) image patch pairs with respect to their individual dictionaries. Adaptive regularised l2‐boosting algorithm is proposed to learn this type of mapping function. Specifically, to reduce time consumption, the authors cluster training patches into several clusters. Within each cluster, a pair of dictionaries for LR and HR image patches is jointly trained. Adaptive regularised l2‐boosting algorithm is then employed to obtain the function. Thus, in a reconstruction stage, for each given input LR image patch, the authors can effectively estimate its corresponding HR image patch. Their extensive experimental results demonstrated that the proposed method achieves a performance of similar quality performance to that of the top methods. |
| first_indexed | 2024-03-12T00:37:09Z |
| format | Article |
| id | doaj.art-6dacbebc27d14feaa0a532f8cf03a6e5 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:37:09Z |
| publishDate | 2017-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-6dacbebc27d14feaa0a532f8cf03a6e52023-09-15T09:32:59ZengWileyIET Computer Vision1751-96321751-96402017-10-0111751752910.1049/iet-cvi.2016.0274Adaptive regularised l2‐boosting on clustered sparse coefficients for single image super‐resolutionYulan Han0Yongping Zhao1Haifeng Yu2Deparment of Automatic Test and ControlHarbin Institute of TechnologyHarbinPeople's Republic of ChinaDeparment of Automatic Test and ControlHarbin Institute of TechnologyHarbinPeople's Republic of ChinaChina Academy of Space TechnologyBeijingPeople's Republic of ChinaIn this study, the authors propose a novel approach for single image super‐resolution. Their method is based on the idea of learning a mapping function, which can reveal the intrinsic relationship between sparse coefficients of low‐resolution (LR) and high‐resolution (HR) image patch pairs with respect to their individual dictionaries. Adaptive regularised l2‐boosting algorithm is proposed to learn this type of mapping function. Specifically, to reduce time consumption, the authors cluster training patches into several clusters. Within each cluster, a pair of dictionaries for LR and HR image patches is jointly trained. Adaptive regularised l2‐boosting algorithm is then employed to obtain the function. Thus, in a reconstruction stage, for each given input LR image patch, the authors can effectively estimate its corresponding HR image patch. Their extensive experimental results demonstrated that the proposed method achieves a performance of similar quality performance to that of the top methods.https://doi.org/10.1049/iet-cvi.2016.0274adaptive regularised l2-boosting algorithmclustered sparse coefficientssingle image super-resolutionmapping function learninglow-resolution image patchhigh-resolution image patch |
| spellingShingle | Yulan Han Yongping Zhao Haifeng Yu Adaptive regularised l2‐boosting on clustered sparse coefficients for single image super‐resolution IET Computer Vision adaptive regularised l2-boosting algorithm clustered sparse coefficients single image super-resolution mapping function learning low-resolution image patch high-resolution image patch |
| title | Adaptive regularised l2‐boosting on clustered sparse coefficients for single image super‐resolution |
| title_full | Adaptive regularised l2‐boosting on clustered sparse coefficients for single image super‐resolution |
| title_fullStr | Adaptive regularised l2‐boosting on clustered sparse coefficients for single image super‐resolution |
| title_full_unstemmed | Adaptive regularised l2‐boosting on clustered sparse coefficients for single image super‐resolution |
| title_short | Adaptive regularised l2‐boosting on clustered sparse coefficients for single image super‐resolution |
| title_sort | adaptive regularised l2 boosting on clustered sparse coefficients for single image super resolution |
| topic | adaptive regularised l2-boosting algorithm clustered sparse coefficients single image super-resolution mapping function learning low-resolution image patch high-resolution image patch |
| url | https://doi.org/10.1049/iet-cvi.2016.0274 |
| work_keys_str_mv | AT yulanhan adaptiveregularisedl2boostingonclusteredsparsecoefficientsforsingleimagesuperresolution AT yongpingzhao adaptiveregularisedl2boostingonclusteredsparsecoefficientsforsingleimagesuperresolution AT haifengyu adaptiveregularisedl2boostingonclusteredsparsecoefficientsforsingleimagesuperresolution |