The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
권호 표지
DOI QR코드

DOI QR Code

Implementation of Image Improvement using MAD Order Statistics for SAR Image in Wavelet Transform Domain

웨이블렛 변환 영역에서 MAD 순서통계량을 이용한 SAR 영상의 화질개선 구현

  • 이철 (인하공업전문대학 메카트로닉스과) ;
  • 이정석 (인하공업전문대학 메카트로닉스과)
  • Received : 2014.11.15
  • Accepted : 2014.12.15
  • Published : 2014.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper is proposed a wavelet-based the order statistics MAD(Median Absolute Deviation) method of SAR(Synthetic Aperture Radar) image for image enhancement. also The method of compared and defined the threshold the wavelet coefficients using MAD of the wavelet coefficients of the detail subbands was proposed to effectively image enhancement. In order to complement the disadvantage, the threshold of the proposed method sets up the image statistic and excludes the distortion. The hardware design is used FPGA of Xilinx and DSP system for the image enhancement and compressed encoding of the proposed algorithm. Therefore the proposed method is totally verified by comparing with the several other images.

본 논문에서는 지형의 형태 파악에 주로 이용되는 SAR(Synthetic Aperture Radar) 영상의 화질을 저해하는 주된 요소인 잡음을 제거하기 위하여 웨이블렛 변환 기반 MAD순서통계량 알고리즘을 논의한다. 효과적인 영상개선을 위하여 SAR 영상에 근사부분대역의 웨이블렛 계수에 가중평균(Weighted average)법으로 영상처리하고 상세 부분대역의 웨이블렛 계수에 중앙절대편차(MAD : Median Absolute Deviation)를 이용한 임계값을 설정하여 왜곡요소를 제거하는 방법을 제안한다. 특히 제안 방법의 임계값은 잡음과 같은 왜곡요소를 배재하고 영상의 통계량을 고려하여 설정하였다. 제안된 방법은 실시간처리를 보장하기 위하여 DSP와 FPGA를 이용한 하드웨어로 구현하였으며 Xilinx FPGA를 사용하여 실험 하였다.

Keywords

References

  1. S. Frita and M. Boulemden, "Meteorological Image Processing with Wavelet," IEEE ICASSP(Int. Conf. on Acoustics, Speech, and Signal Processing), Sandiego, CA, Apr. 1998, pp. 2997-2300.
  2. S.-U. Kim, "An Image Denoising Algorithm Using Multiple Images for Mobile Smartphone Cameras," J. of the Korea Institute of Electronic Communication Science, vol. 11. no. 3, 2014, pp. 1189-1195. https://doi.org/10.13067/JKIECS.2014.9.10.1189
  3. P. Schelkens, The JPEG2000 Suite. New York : Wiley, 2009.
  4. S. C. Burrus, R. A. Gopinath, and H. Guo, Introduction to Wavelets and Wavelet transforms : A Primer. New Jersey : Prentice-Hall, 1998.
  5. P. J. Burt and R. J. Kolezynski, "Enhanced image capture through fusion," Proc. of 4th Inter-national Conf. on Computer Vision, New York, NY, Apr. 1993, pp. 173-182.
  6. H. Ma, C. Jia, and S. Liu, "Multisource image fusion based on wavelet transform," Int. J. of Information Technology, vol. 3, no. 1, 2005, pp. 81-91.
  7. C.-K. Lee and D.-I. Kim, "Adaptive Noise Reduction of Speech Using Wavelet Transform," J. of the Korea Institute of Electronic Communication Science, vol. 4, no. 3, 2009, pp. 190-196.
  8. Y.-Y. Kim, "Progressive Image Coding using Wavelet Transform," J. of the Korea Institute of Electronic Communication Science, vol. 9, no. 1, 2013, pp. 33-40. https://doi.org/10.13067/JKIECS.2014.9.1.33
  9. J.-H. Lee, "A Study for Sales and Demand Forecasting Model Using Wavelet Neural Networks," J. of the Korea Institute of Electronic Communication Science, vol. 9, no. 1, 2013, pp. 131-136. https://doi.org/10.13067/JKIECS.2014.9.1.131