Fast super-resolution algorithm using rotation-invariant ELBP classifier and hierarchical pattern matching |
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| Affiliation: | 1. School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China;2. School of Computing, National University of Singapore, Singapore;1. Department of Computer Science, University of California, Irvine, USA;2. School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, China;3. Faculty of Information and Communication Technology, Mahidol University, Thailand;1. Beijing Key Lab of Intelligent Telecomm. Software and Multimedia, Beijing University of Posts and Telecomm., Beijing 100876, China;2. School of Information Technology, Deakin University, Melbourne, VIC 3125, Australia;3. Department of Computer Science and Technology, Tsinghua University, Beijing, China;1. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, Guangdong 510006, China;2. Department of Computer Science, Rutgers University, NJ 08854-8019, USA;3. School of Computer Engineering, Nanyang Technological University, 639798 Singapore, Singapore;4. School of Electronic Engineering, Xidian University, Xi’an, Shaanxi 710071, China;5. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, Guangdong 510642, China;1. Department of Computer Science and Information Engineering, National Dong Hwa University, Taiwan;2. Department of Computer Science, Jinan University, Guangzhou, China;3. Nanjing University of Information Science & Technology, Nanjing, China;4. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China |
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| Abstract: | This paper proposes a fast super-resolution (SR) algorithm using content-adaptive two-dimensional (2D) finite impulse response (FIR) filters based on a rotation-invariant classifier. The proposed algorithm consists of a learning stage and an inference stage. In the learning stage, we cluster a sufficient number of low-resolution (LR) and high-resolution (HR) patch pairs into a specific number of groups using the rotation-invariant classifier, and choose a specific number of dominant clusters. Then, we compute the optimal 2D FIR filter(s) to synthesize a high-quality HR patch from an LR patch per cluster, and finally store the patch-adaptive 2D FIR filters in a dictionary. Also, we present a smart hierarchical addressing method for effective dictionary exploration in the inference stage. In the inference stage, the ELBP of each input LR patch is extracted in the same way as the learning stage, and the best matched FIR filter(s) to the input LR patch is found from the dictionary by the hierarchical addressing. Finally, we synthesize the HR patch by using the optimal 2D FIR filter. The experimental results show that the proposed algorithm produces better HR images than the existing SR methods, while providing fast running time. |
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| Keywords: | Reconstruction Super-resolution Classification FIR filters Up-scaling Hierarchical addressing |
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