Journal of the Korean Society for Aeronautical & Space Sciences (한국항공우주학회지)

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
권호 표지
DOI QR코드

DOI QR Code

Application of Multi-Frame Based Super-Resolution Algorithm for a Color Recognition Enhancement for the UAV

복수영상기반 초해상도 색상인식능력향상 알고리즘의 무인기 적용

  • Park, Jihoon (Department of Aerospace Engineering, Busan National University) ;
  • Kim, Jeongho (Department of Aerospace Engineering, Busan National University) ;
  • Lee, Daewoo (Department of Aerospace Engineering, Busan National University)
  • Received : 2017.02.03
  • Accepted : 2017.02.27
  • Published : 2017.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper describes the application of Multi-frame based super-resolution method to enhance resolution of image information from the UAV, and the improvement of UAV's ground target recognition ability. To verify this algorithm, we designed a flight/ground control system, and the UAV, and then the algorithm was validated using the UAV system with ground target. As a result of the comparison between the pre-applied image and post-applied one shows that the RMSE is from 0.0677 to 0.0315, NRMSE is from 7.4030% to 3.5726%, PSNR is from 23.3885dB to 30.0036dB, and SSIM is from 0.6996 to 0.8948. Through these results, we validate this study can enhance the resolution of UAV's image using Multi-frame based super-resolution algorithm.

본 논문은 무인기를 통해 획득한 영상정보의 해상도를 향상시키기 위해 복수영상 기반 초해상도 기법을 적용하고, 이를 통한 무인기의 지상 목표물 인식 능력 향상에 대한 내용을 담고 있다. 이를 검증하기 위해 기체 제작 및 제어 시스템을 구축하였고, 무인기와 지상의 목표물을 이용해 설계한 알고리즘을 검증하였다. 실험 결과 초해상도 기법 적용 전, 후 영상을 비교하였을 때 RMSE는 0.0677에서 0.0315, NRMSE는 7.4030%에서 3.5726%로, PSNR은 23.3885dB에서 30.0036dB, SSIM은 0.6996에서 0.8948로 향상되었음을 확인하였다. 이를 통해 본 알고리즘을 적용하여 무인기 영상의 해상도를 향상시킬 수 있음을 검증하였다.

Keywords

References

  1. T. Lehmann, C. Gonner, and K. Spitzer, "Survey: interpolation methods in medical image processing," IEEE Trans. Medical Imaging, vol1. 18, no. 11, November 1999, pp. 1049-1075.
  2. W. Freeman, T. Jones, and E. Pasztor, "Example-based super-resolution," IEEE Computer Graphics and Applications, vol. 22, no.2, pp. 56-65, March/April 2002. https://doi.org/10.1109/38.988747
  3. D. Glasner, S. Bagon, and M. Irani, "Super-resolution from a single image," Proc. IEEE Int. Conf. Computer Vision, pp. 349-356, September 2009.
  4. G. Freedman and R. Fattal, "Image and video upscaling from local self-examples," ACM Trans. Graphics, vol. 30, no. 2, pp. 12.1-12.11, April 2011.
  5. COMANICIU, Dorin; RAMESH, Visvanathan; MEER, Peter. Real-time tracking of non-rigid objects using mean shift. In: Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on. IEEE, 2000. p. 142-149.
  6. S. Farsiu, M. Robinson, M. Elad, and P. Milanfar, "Fast and robust multiframe super resolution," IEEE Trans. Image Processing, vol. 13, no. 10, pp. 1327-1344, October 2004. https://doi.org/10.1109/TIP.2004.834669
  7. T. Lehmann, C. Gonner, and K. Spitzer, "Survey: interpolation methods in medical image processing," IEEE Trans. Medical Imaging, vol1. 18, no. 11, pp. 1049-1075, November 1999.
  8. T. S. Huang and R. Y. Tsai, "Multi-frame image restoration and registration," Adv. Comput. Vis. Image Process., vol. 1, 1984, pp. 317-339.
  9. W. Bai, J. Liu, M. Li, and Z. Guo, "Multi-frame super-resolution using refined exploration of extensive self-examples," MMM, pp. 403-413, January January 2013.
  10. Jeong, Seokhwa, Inhye Yoon, and Joonki Paik. "UHD TV Image Enhancement using Multi-frame Example-based Super-resolution." Journal of the Institute of Electronics and Information Engineers 52.3 (2015): 154-161. https://doi.org/10.5573/ieie.2015.52.3.154
  11. Zhao, Nan, et al. "Multi-Frame Image Super-Resolution Based on Regularization Scheme." Control, Automation and Systems Engineering (CASE), 2011 International Conference on. IEEE, 2011.
  12. Shin, Jeongho. "Superresolution Restoration From Directional Rectangular Blurred Images." Journal of Broadcast Engineering 19.1 (2014): 109-117. https://doi.org/10.5909/JBE.2014.19.1.109
  13. MOUSTAFA, Marwa, et al. Satellite Super Resolution Image Reconstruction Based on Parallel Support Vector Regression. In: International Conference on Advanced Machine Learning Technologies and Applications. Springer International Publishing, 2014. p. 223-235.