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Image Super-Resolution Reconstruction Based on Sparse Representation and POCS Method

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

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Abstract

The traditional projection onto convex set (POCS) algorithm can reconstruct a low resolution (LR) image, but it is contradictory in retaining image detail and denoising, so the quality of a reconstructed image is limited. To avoid defects of POCS and obtain higher resolution, the image denoising idea based on sparse representation is led into this paper. Sparse representation can learn well the optimized overcomplete sparse dictionary of an image, which has self-adaptive property to image data and can describe image essential features so as to implement the goal of denoising efficiently. At present, K- singular value decomposition (K-SVD) is the emerging image processing method of sparse representation and has been used widely in image denoising. Therefore, combined the advantages of K-SVD and POCS, a new image ISR method is explored here. In terms of signal noise ratio (SNR) values and the visual effect of reconstructed images, simulation results show that our method proposed has clear improvement in image resolution and can retain image detail well.

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Acknowledgements

This work was supported by the grants from National Nature Science Foundation of China (Grant No. 61373098 and 61370109), the grant from Natural Science Foundation of Anhui Province (No. 1308085MF85).

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Authors and Affiliations

  1. Department of Communication Technology, College of Electronic Information Engineering, Suzhou Vocational University, Suzhou, 215104, Jiangsu, China

    Li Shang & Shu-fen Liu

  2. School of Electrical Engineering and Automation, Anhui University, Hefei, 230039, Anhui, China

    Zhan-li Sun

Authors
  1. Li Shang
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  2. Shu-fen Liu
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  3. Zhan-li Sun
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Corresponding author

Correspondence to Li Shang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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© 2015 Springer International Publishing Switzerland

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Shang, L., Liu, Sf., Sun, Zl. (2015). Image Super-Resolution Reconstruction Based on Sparse Representation and POCS Method. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_34

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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