Journal of Advanced Navigation Technology (한국항행학회논문지)

The Korean Navigation Institute (한국항행학회)
권호 표지
DOI QR코드

DOI QR Code

Performance Improvement of Aerial Images Taken by UAV Using Daubechies Stationary Wavelet

Daubechies 정상 웨이블릿을 이용한 무인항공기 촬영 영상 성능 개선

  • Kim, Sung-Hoon (Department of Aircraft System Engineering, Hanseo University) ;
  • Hong, Gyo-Young (Department of Aircraft System Engineering, Hanseo University)
  • 김성훈 (한서대학교 항공시스템공학과) ;
  • 홍교영 (한서대학교 항공시스템공학과)
  • Received : 2016.11.01
  • Accepted : 2016.12.13
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we study the technique to improve the performance of the aerial images taken by UAV using daubechies stationary wavelet transform. When aerial images taken by UAV were damaged by gaussian noise very commonly applied, the experiment for image performance improvement was performed. It was known that stationary wavelet transform is the transferring solution to the problem occurred by down sampling from DWT also more efficient to remove noise than DWT. Also haar wavelet is discontinuous function so not efficient for smooth signal and image processing. Therefore, this study is confirmed that the noise can be removed by daubechies stationary wavelet and the performance is improved by haar stationary wavelet.

본 논문은 Daubechies 정상 웨이블릿 변환을 이용하여 무인항공기 항공촬영 영상의 성능을 향상하기 위한 기법에 대해 연구하였다. 무인항공기에서 획득된 영상이 가장 일반적이고 보편적으로 적용되는 가우시안 잡음에 의하여 손상되었을 경우, 영상의 성능을 개선하기 위한 실험을 수행하였다. 정상 웨이블릿 변환은 DWT (discrete wavlet transform)에서 다운샘플링에 의해 발생하는 문제점을 해결하기 위한 변환방법으로써 잡음제거에 DWT보다 효과적이라고 알려져 있다. 또한 Haar 웨이블릿은 불연속 함수인 이유로 매끄러운 신호나 영상처리에 효과적이지 못하다. 이에 본 연구에서는 daubechies 정상 웨이블릿을 이용하여 잡음을 제거하였으며 기존 haar 정상 웨이블릿을 적용하였을 때 보다 더 성능이 개선됨을 확인하였다.

Keywords

References

  1. H. M. Kim, A study on real-time prediction algorithm of aviation imaging shooting area, Master's degree Thesis, Korea Polytechnic University, Gyeonggi-do, Korea, Dec, 2012.
  2. H. R. Yu, Study for the noise rejection threshold value decision with using band information of stationary wavelet, Master's degree Thesis, Daejeon University, Daejeon, Koera, Aug, 2007.
  3. H. Y. Ryu, K. W. Lee, and B. D. Kwon, "Noise rejection at satellite images using wavelet filter," in Autumn conference on The Korean Earth Science Society, Seoul: Korea, pp. 398-405, 2005.
  4. H. S. Lee, Blind estimation of hop timing and duration of FHSS systems, Master's degree Thesis, Chungnam National University, Chungcheongnam-do, Korea, Feb, 2010.
  5. M. I. Choi, and J. C. Kim, "A study on the comparison of denoising performance of stationary wavelet transform for discharge signal data in cast-resin transformer," Journal of the Korean Institute of Illuminating and Electrical Installation Engineers, Vol. 28, No. 3, pp. 84-90, Mar. 2014.
  6. Y. Chen, and H. Ma, "Signal de-noising in ultrasonic testing based on stationary wavelet transform," Intelligent Systems, Vol. 2, No. 10, pp. 474-478, May. 2009.
  7. J. E. Lee and I. S. Kim, "A study on the fault detection technique of the grid-connected photovoltaic system using wavelet transformation," The Korean Institute of Power Electronics, Vol. 16, No. 1, pp. 79-87, Feb. 2011. https://doi.org/10.6113/TKPE.2011.16.1.79
  8. Y. O. Park, A study on the image denoising based on wavelet packet, Master's degree Thesis, Mokwon University, Daejeon, Korea, Dec, 2002.
  9. N. C. Park, and C. Y. Woo, "Denoising images by visushrink technique using the estimated noise power in the highest equal subband of wavelet," The Korea Institute of Signal Processing and Systems, Vol. 13, No. 1, pp. 26-31, Jan. 2010.
  10. H. J. Nam, S. K. Choi, S. S. Shin, and Y. H. Cho, "Noise reduction of digital image using wavelet coefficient," in Spring Conference on The Korea Contents Society, Daejeon: Korea, pp. 376-382, May. 2003.

Cited by

  1. 그래픽 프로세서를 이용한 병렬연산 기반 해무 제거 고속화 vol.21, pp.5, 2016, https://doi.org/10.12673/jant.2017.21.5.485