Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Patch Information based Linear Interpolation for Generating Super-Resolution Images in a Single Image

단일이미지에서의 초해상도 영상 생성을 위한 패치 정보 기반의 선형 보간 연구

  • Han, Hyun-Ho (Dept of Plasma Bio Display, KwangWoon University) ;
  • Lee, Jong-Yong (Ingenium college of liberal arts, Kwangwoon university) ;
  • Jung, Kye-Dong (Ingenium college of liberal arts, Kwangwoon university) ;
  • Lee, Sang-Hun (Ingenium college of liberal arts, Kwangwoon university)
  • 한현호 (광운대학교 플라즈마 바이오 디스플레이학과) ;
  • 이종용 (광운대학교 인제니움학부대학) ;
  • 정계동 (광운대학교 인제니움학부대학) ;
  • 이상훈 (광운대학교 인제니움학부대학)
  • Received : 2018.04.04
  • Accepted : 2018.06.20
  • Published : 2018.06.28
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Abstract

In this paper, we propose a linear interpolation method based on patch information generated from a low - resolution image for generating a super resolution image in a single image. Using the regression model of the global space, which is a conventional super resolution generation method, results in poor quality in general because of lack of information to be referred to a specific region. In order to compensate for these results, we propose a method to extract meaningful information by dividing the region into patches in the process of super resolution image generation, analyze the constituents of the image matrix region extended for super resolution image generation, We propose a method of linear interpolation based on optimal patch information that is searched by correlating patch information based on the information gathered before the interpolation process. For the experiment, the original image was compared with the reconstructed image with PSNR and SSIM.

본 논문은 단일 이미지에서 초해상도 영상 생성을 위해 저해상도 이미지에서 생성한 패치정보를 기반으로 선형보간하는 방법을 제안하였다. 기존의 초해상도 생성 방법인 전역 공간의 회귀 모델을 사용하면 특정 영역에 대해 참조할 정보가 부족하여 일반적으로 품질이 떨어지는 결과가 나타난다. 이러한 결과를 보완하기 위해 제안하는 방법은 초해상도 이미지 생성 과정에서 영상을 패치 단위로 지역을 분할하여 의미있는 정보를 수집하고, 수집된 정보를 기반으로 초해상도 이미지 생성을 위해 확장시킨 이미지 매트릭스 영역의 구성정보를 분석하여 선형 보간 과정을 거치고 패치정보를 대응시켜 탐색한 최적의 패치 정보를 기준으로 선형 보간하는 방법을 제안하였다. 실험을 위해 원본 이미지를 복원된 영상과 PSNR, SSIM으로 비교 평가하였다.

Keywords

References

  1. S. H. Han, Y. P. Hong & S. H Lee. (2012). Saliency map creation method robust to the contour of objects. Journal of Digital Convergence, Vol. 10, No. 3, pp. 173-178. https://doi.org/10.14400/JDPM.2012.10.3.173
  2. G. O. Kim, G. S. Lee & S. H. Lee. (2014). An edge extraction method using k-means clustering in image. Journal of Digital Convergence, Vol. 12, No. 11, pp. 281-288. https://doi.org/10.14400/JDC.2014.12.11.281
  3. M. K. Kwon & H. S. Yang. (2017). A scene search method based on principal character identification using convolutional neural network. Journal of Convergence for Information Technology, Vol. 7, No. 2, pp. 31-36. https://doi.org/10.22156/CS4SMB.2017.7.2.031
  4. J. C. Han, B. C. Koo & K. J. Cheoi. (2017), Obstacle detection and recognition system for autonomous ariving vehicle. Journal of Convergence for Information Technology, Vol. 7, No. 6, pp. 229-235. https://doi.org/10.22156/CS4SMB.2017.7.6.229
  5. H. H. Han, G. S. Lee, Y. S Park & S. H. Lee. (2017). Estimate saliency map based on multi feature assistance of learning algorithm. Journal of the Korea Convergence Society. Vol. 8, No. 6, pp. 29-36. https://doi.org/10.15207/JKCS.2017.8.6.029
  6. Rasti, P., Nasrollahi, K., Orlova, O., Tamberg, G., Ozcinar, C., Moeslund, T. B., & Anbarjafari, G. (2017). A new low-complexity patch-based image super-resolution. IET Computer Vision 11.7 : 567-576. https://doi.org/10.1049/iet-cvi.2016.0463
  7. Zhang, K., Wang, B., Zuo, W., Zhang, H., & Zhang, L. (2016). Joint learning of multiple regressors for single image super-resolution. IEEE Signal Processing Letters, 23(1), 102-106. https://doi.org/10.1109/LSP.2015.2504121
  8. Mokari, A., & Ahmadyfard, A. (2016). Fast single image SR via dictionary learning. IET Image Processing, 11(2), 135-144. https://doi.org/10.1049/iet-ipr.2016.0422
  9. Yang, J., Wang, Z., Lin, Z., Cohen, S., & Huang, T. (2012). Coupled dictionary training for image super-resolution. IEEE transactions on image processing, 21(8), 3467-3478. https://doi.org/10.1109/TIP.2012.2192127
  10. Freeman, W. T., Jones, T. R., & Pasztor, E. C. (2002). Example-based super-resolution. IEEE Computer graphics and Applications, 22(2), 56-65. https://doi.org/10.1109/38.988747
  11. Zhao, L., Sun, Q., & Zhang, Z. (2017). Single Image Super-Resolution Based on Deep Learning Features and Dictionary Model. IEEE Access, 5, 17126-17135. https://doi.org/10.1109/ACCESS.2017.2736058
  12. Huang, J. J., Liu, T., Dragotti, P. L., & Stathaki, T. (2017, July). SRHRF+: Self-Example Enhanced Single Image Super-Resolution Using Hierarchical Random Forests. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 71-79).
  13. Dong, W., Zhang, L., Shi, G., & Wu, X. (2011). Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Transactions on Image Processing, 20(7), 1838-1857. https://doi.org/10.1109/TIP.2011.2108306
  14. Zeyde, R., Elad, M., & Protter, M. (2010, June). On single image scale-up using sparse-representations. In International conference on curves and surfaces (pp. 711-730). Springer, Berlin, Heidelberg.
  15. Timofte, R., De, V., & Van Gool, L. (2013, December). Anchored neighborhood regression for fast example-based super-resolution. In Computer Vision (ICCV), 2013 IEEE International Conference on (pp. 1920-1927). IEEE.
  16. Timofte, R., De Smet, V., & Van Gool, L. (2014, November). A+: Adjusted anchored neighborhood regression for fast super-resolution. In Asian Conference on Computer Vision (pp. 111-126). Springer, Cham.
  17. Glasner, D., Bagon, S., & Irani, M. (2009, September). Super-resolution from a single image. In Computer Vision, 2009 IEEE 12th International Conference on (pp. 349-356). IEEE.
  18. Yang, J., Wright, J., Huang, T. S., & Ma, Y. (2010). Image super-resolution via sparse representation. IEEE transactions on image processing, 19(11), 2861-2873. https://doi.org/10.1109/TIP.2010.2050625