dc.contributor.author | Lakshminarayanan, K | |
dc.contributor.author | Santhana Krishnan, R | |
dc.contributor.author | Golden Julie, E | |
dc.contributor.author | Robinson, Yesudhas Harold | |
dc.contributor.author | Kumar, Raghvendra | |
dc.contributor.author | Son, Le Hoang | |
dc.contributor.author | Hung, Trinh Xuan | |
dc.contributor.author | Samui, Pijush | |
dc.contributor.author | Ngo, Phuong Thao Thi | |
dc.contributor.author | Tien Bui, Dieu | |
dc.date.accessioned | 2021-05-03T12:32:41Z | |
dc.date.available | 2021-05-03T12:32:41Z | |
dc.date.created | 2020-01-27T12:34:39Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Lakshminarayanan, K., Santhana Krishnan, R., Golden Julie, E., Harold Robinson, Y., Kumar, R., Son, L. H., ... & Tien Bui, D. (2020). A new integrated approach based on the iterative super-resolution algorithm and expectation maximization for face hallucination. Applied Sciences, 10(2). | en_US |
dc.identifier.issn | 2076-3417 | |
dc.identifier.uri | https://hdl.handle.net/11250/2753289 | |
dc.description.abstract | This paper proposed and verified a new integrated approach based on the iterative super-resolution algorithm and expectation-maximization for face hallucination, which is a process of converting a low-resolution face image to a high-resolution image. The current sparse representation for super resolving generic image patches is not suitable for global face images due to its lower accuracy and time-consumption. To solve this, in the new method, training global face sparse representation was used to reconstruct images with misalignment variations after the local geometric co-occurrence matrix. In the testing phase, we proposed a hybrid method, which is a combination of the sparse global representation and the local linear regression using the Expectation Maximization (EM) algorithm. Therefore, this work recovered the high-resolution image of a corresponding low-resolution image. Experimental validation suggested improvement of the overall accuracy of the proposed method with fast identification of high-resolution face images without misalignment. | en_US |
dc.language.iso | eng | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © The Author(s) 2020. | en_US |
dc.source.volume | 10 | en_US |
dc.source.journal | Applied Sciences | en_US |
dc.source.issue | 2 | en_US |
dc.identifier.doi | https://doi.org/10.3390/app10020718 | |
dc.identifier.cristin | 1782901 | |
dc.source.articlenumber | 718 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |