Face hallucination based on two-dimensional joint learning |
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Affiliation: | 1. China Ship Development and Design Center, Wuhan 430064, China;2. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, China;2. School of Computer Science, Carnegie Mellon University, United States;1. Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA;2. Liberty Mutual Insurance, Boston, MA, USA |
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Abstract: | In this paper, a face hallucination method based on two-dimensional joint learning is presented. Unlike the existing works on face super-resolution algorithms that first reshape the image or image patch into 1D vector, in our study the spatial construction of the high resolution (HR) and the low resolution (LR) face image are efficiently maintained in the reconstruction procedure. Enlightened by the 1D joint learning approach for image super-resolution, we propose a 2D joint learning algorithm to map the original 2D LR and HR image patch spaces onto a unified feature subspace. Subsequently, the neighbor-embedding (NE) based super-resolution algorithm can be conducted on the unified feature subspace to estimate the reconstruction weights. With these weights, the initial HR facial image can be generated. To refine further the initial HR estimate, the global reconstruction constraint is exploited to improve the quality of reconstruction result. Experiments on the face databases and real-world face images demonstrate the effectiveness of the proposed algorithm. |
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Keywords: | Face hallucination Two dimensional coupled constraint Two dimensional joint learning Maximum a posteriori |
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