Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

Implementation of a Real-Time Spatio-Temporal Noise Reduction System

실시간 시공 노이즈 제거 시스템 구현

  • Hong, Hye-Jeong (School of Electrical & Electronic Engineering, Yonsei University) ;
  • Kim, Hyun-Jin (School of Electrical & Electronic Engineering, Yonsei University) ;
  • Kang, Sung-Ho (School of Electrical & Electronic Engineering, Yonsei University)
  • 홍혜정 (연세대학교 전기전자공학과) ;
  • 김현진 (연세대학교 전기전자공학과) ;
  • 강성호 (연세대학교 전기전자공학과)
  • Published : 2008.03.25
Download PDF
⟨ Previous Next ⟩

Abstract

Spatio-temporal filters are capable of reducing noise from moving pictures, which cannot be dealt with by spatial filters. However, the algorithm is too complicated to be realized as hardware. We implemented a real-time spatio-temporal noise reduction system, using at most three frames, based upon adaptive mean filter algorithm. Some factors which interfere with hardware implementation were modified. Noise estimated from the previous frame was used to filter the current frame so that filtering could be conducted in parallel with noise estimation. This speeds up the system thereby enabling real-time execution. The form of filtering windows was also modified to facilitate synchronization. The proposed structure was implemented on Virtex 4 XC4VLX60, occupying 66% of total slices with 80MHz of the maximum operation frequency.

시공필터는 공간필터로는 제거할 수 없는 동영상의 노이즈를 제거하지만 알고리듬이 매우 복잡하여 하드웨어로 구현하기에 부적절하다. 본 논문에서는 적응 평균필터 알고리듬을 바탕으로 최대 세 장의 프레임을 사용하는 실시간 시공 노이즈 제거 시스템을 구현한다. 기존의 알고리듬에서 하드웨어로 구현하기에 부적절한 요소들을 수정하였다. 동작 속도를 높이기 위해서 노이즈 추정과 필터링이 병렬적으로 수행되도록 이전 프레임에서 추정된 노이즈를 현재 프레임 필터링에 이용하게 하였다. 또한 필터링 윈도우의 형태를 변형하여 시스템의 동기화를 용이하게 하였다. 제안하는 구조는 Virtex 4 XC4VLX60 상에 구현하였고 총 66%의 슬라이스를 사용하고 최대 80MHz의 속도로 동작하였다.

Keywords

References

  1. S. V. Vaseghi, 'Advanced digital signal processing and noise reduction.' John Wiley & Sons Ltd., pp. 29, 2000
  2. R. C. Gonzales and R. E. Woods, 'Digital Image Processing,' Prentive-Hall, Inc., pp75-77, 2002
  3. B. E. Shi, 'Spatio-temporal image filtering with cellular neural networks,' in Proc. of IEEE　International Conference on Neural Networks, pp. 1410-1415, 1996
  4. B. Smolka, M. Szczepanski, K. N. Plataniotis and A. N. Venetsanopoulos, 'On the efficiency of random walk approach to noise reduction in color images,' in Proc. of IEEE　International Conference on Image Processing, pp. 138-141, Thessaloniki, Greece, October 2001
  5. H. Hisamitsu and K. Arakawa, 'Time and spatial e-filter for improving image quality of compressed video,' in Proc. IEEE International Midwest Symposium on Circuits and Systems, pp. 289-292, Hiroshima, Japan, July 2004
  6. I. Gasteratos, A. Gasteratos and I. Andreadis, 'An Algorithm for Adaptive Mean Filtering and Its Hardware Implementation,' Journal of VLSI Signal Processing, Vol. 44, pp. 63-78, August 2006 https://doi.org/10.1007/s11265-006-5920-3
  7. S-W. Lee, V. Maik, J. Jang, J. Shin and J. Paik, 'Noise-Adaptive Spatio-Temporal Filter for Real-Time Noise Removal in Low Light Level Images,' IEEE Trans. Consumer Electronics, Vol. 5, Issue 2, pp648-653, May 2005
  8. D-H. Shin, R-H Park, S. Yang and J-H Jung, 'Block-based noise estimation using adaptive Gaussian filtering,' IEEE Transactions on Consumer Electronics, Vol. 51, No. 1, February 2005