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Face hallucination with K-means++ dictionary learning

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Abstract

Interested face regions have the low-resolution problem in the video surveillance because the distance between face and camera is far. Thus, the high-resolution (HR) faces need to be reconstructed from low-resolution (LR) faces for further processing. Typical face hallucination based on patch-wise sparse coding can achieve better results but have very high complexity for training. In order to reduce the complexity, this paper proposes a method which uses K-means++ clustering instead of sparse coding to obtain an over-complete dictionary pair. Then, the least angle regression (LARS) algorithm is utilized to calculate the coefficients and reconstruct the high-resolution faces. The experimental results show that proposed algorithm can effectively reduce complexity in condition of irregular LR faces. In addition, the comparisons also prove that the proposed method can improve the value of PSNR and SSIM in the same database.

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Abbreviations

HR:

high-resolution

LR:

low-resolution

LARS:

least angle regression

ScDL:

semi-coupled dictionary learning

SVD:

singular value decomposition

ScSR:

sparse coding superresolution

ZCA:

Zero Components Analysis

PSNR:

Peak Signal to Noise Ratio

SSIM:

Structural Similarity

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Acknowledgments

The author would like to thank Mengxue Wang from Shandong University for revising this paper. This work was supported in part by the National Key R&D Program of China (2019YFB1311001), in part by the National Natural Science Foundation of China (61203261 and 61876099), in part by the Scientific and Technological Development Project of Shandong Province (2019GSF111002), in part by the Shenzhen Science and Technology Research and Development Funds (JCYJ20180305164401921), in part by the Foundation of Ministry of Education Key Laboratory of System Control and Information Processing (Scip201801), in part by the Foundation of Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education (2018ICIP03), and in part by the Foundation of State Key Laboratory of Integrated Services Networks (ISN20-06).

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Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, 250061, China

    Zhenxue Chen, Jiadi Li & Chengyun Liu

  2. Shenzhen Research Institute of Shandong University, Shandong University, Shenzhen, 518057, China

    Zhenxue Chen

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  1. Zhenxue Chen
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  2. Jiadi Li
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  3. Chengyun Liu
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Correspondence to Zhenxue Chen.

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Chen, Z., Li, J. & Liu, C. Face hallucination with K-means++ dictionary learning. Multimed Tools Appl 79, 11685–11698 (2020). https://doi.org/10.1007/s11042-019-08505-0

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