A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

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ID: 110049
2020
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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.
Reference Key
lakshminarayanan2020applieda Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors K. Lakshminarayanan;R. Santhana Krishnan;E. Golden Julie;Y. Harold Robinson;Raghvendra Kumar;Le Hoang Son;Trinh Xuan Hung;Pijush Samui;Phuong Thao Thi Ngo;Dieu Tien Bui;Lakshminarayanan, K.;Santhana Krishnan, R.;Golden Julie, E.;Harold Robinson, Y.;Kumar, Raghvendra;Son, Le Hoang;Hung, Trinh Xuan;Samui, Pijush;Ngo, Phuong Thao Thi;Tien Bui, Dieu;
Journal applied sciences
Year 2020
DOI
10.3390/app10020718
URL
https://www.mdpi.com/2076-3417/10/2/718/htm
Keywords
hallucination global sparse representation expectation maximization

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