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Facial Image Super-Resolution Reconstruction Based on Separated Frequency Components
- Facial Image Super-Resolution Reconstruction Based on Separated Frequency Components
- Kim, Hyunduk; Lee, Sang-Heon; Sohn, Myoung-Kyu; Kim, Dong-Ju; Kim, Byungmin
- DGIST Authors
- Lee, Sang-Heon; Sohn, Myoung-Kyu
- Issue Date
- IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E96A(6), 1315-1322
- Article Type
- Algorithms; Bilateral Filter; Bilateral Filters; Facial Super Resolution; Frequency Domain; Frequency Domains; Image Reconstruction; Regularization Technique; Separation; Sparse Representation; Super Resolution
- Super resolution (SR) reconstruction is the process of fusing a sequence of low-resolution images into one high-resolution image. Many researchers have introduced various SR reconstruction methods. However, these traditional methods are limited in the extent to which they allow recovery of high-frequency information. Moreover, due to the selfsimilarity of face images, most of the facial SR algorithms are machine learning based. In this paper, we introduce a facial SR algorithm that combines learning-based and regularized SR image reconstruction algorithms. Our conception involves two main ideas. First, we employ separated frequency components to reconstruct high-resolution images. In addition, we separate the region of the training face image. These approaches can help to recover high-frequency information. In our experiments, we demonstrate the effectiveness of these ideas. Copyright © 2013 The Institute of Electronics, Information and Communication Engineers.
- Institute of Electronics, Information and Communication Engineers
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