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Single Image Super Resolution Through Multi Extreme Learning Machine Regressor Fusion

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Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 663))

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Abstract

Single image super resolution (SISR) aims to generate a high resolution (HR) image based on a given low resolution (LR) input. The edge priori based SISR methods tend to estimate HR image by edge-preserving constraint. In this paper, a novel learning based SISR method is proposed to reconstruct HR image by using joint HR gradient field and high frequency constraint. In the training phase, interpolated training LR patches with similar structure are partitioned into the same cluster by K-means clustering, and the Extreme Learning Machine (ELM) are used to get gradient and high frequency regressors in each cluster by training LR/HR patch pairs. In the prediction phase, multi-ELM regressor fusion strategy is used to estimate more accurate gradient and high frequency data, in which the fusion weights are based on the distance of the cluster centers with patch isotropic characteristics. Then, the estimated HR image gradient and high frequency are regarded as a joint constraints priori to reconstruct HR image. Experimental results demonstrate that the proposed method achieves better estimating accuracy of gradient and high frequency and have competitive SR quality compared with the other state-of-the-art SISR methods.

Z. Gan—This research was supported in part by the National Nature Science Foundation, P. R. China. (No. 61071166, 61172118, 61071091, 61471201), Jiangsu Province Universities Natural Science Research Key Grant Project (No. 13KJA510004), and the “1311” Talent Plan of NUPT.

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Authors and Affiliations

  1. Jiangsu Provincial Key Lab of Image Processing and Image Communication, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Xiang Wang, Zongliang Gan, Lina Qi, Changhong Chen & Feng Liu

Authors
  1. Xiang Wang
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  2. Zongliang Gan
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  3. Lina Qi
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  4. Changhong Chen
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  5. Feng Liu
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Corresponding author

Correspondence to Zongliang Gan .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Wang, X., Gan, Z., Qi, L., Chen, C., Liu, F. (2016). Single Image Super Resolution Through Multi Extreme Learning Machine Regressor Fusion. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_12

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  • DOI: https://doi.org/10.1007/978-981-10-3005-5_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3004-8

  • Online ISBN: 978-981-10-3005-5

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