| Autor: |
Lakshminarayanan, K., Krishnan, R. Santhana, Julie, E. Golden, Robinson, Y. Harold, Kumar, Raghvendra, Le Hoang Son, Trinh Xuan Hung, Pijush Samui, Phuong Thao Thi Ngo, Dieu Tien Bui |
| Předmět: |
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| Zdroj: |
Applied Sciences (2076-3417); 1/15/2020, Vol. 10 Issue 2, p718, 22p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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