A novel correspondence-based face-hallucination method |
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| Affiliation: | 1. Department of Electrical and Computer Engineering, Carnegie Mellon University, United States;2. Department of Electronics and Information Engineering, The Hong Kong Polytechnic University, Hong Kong;1. Computing Department, Imperial College, London, UK;2. Department of Computer Science, Rutgers University, USA;1. Authentic Vision GmbH, Austria;2. Department of Computer Sciences, University of Salzburg, Austria;1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou350116, China;2. Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China;3. Tan Kah Kee College, Xiamen University, Zhangzhou363105, China;1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China;2. Beijing Key Laboratory of Network System and Network Culture, Beijing University of Posts and Telecommunications, Beijing, China |
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| Abstract: | This paper addresses the problem of estimating high-resolution (HR) facial images from a single low-resolution (LR) input. We assume that the input LR and estimated HR images are under the same view-point and illumination condition, i.e. the setting of image super-resolution. At the core of our techniques is that the facial images can be decomposed as a texture vector, characterized in terms of the appearance, and a shape vector, characterized in terms of the geometry variations. This enables a two-stage successive estimation framework that is geometry aware and obviates the needs in sophisticated optimizations. In particular, the proposed technique first solves for appearance of the HR faces form the correspondence derived between an interpolated LR face and its corresponding HR face. Given the texture of the HR faces, we incorporate optical flow to solve the local structure at sub-pixel level for the HR faces; here, we use additional geometry inspired priors to further regularize the solution. Experimental results show that our method outperforms other state-of-the-art methods in terms of retaining the facial-feature shape and the estimation of novel features. |
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