DOI QR코드

DOI QR Code

Adaptive Noise Reduction Algorithm for Image Based on Block Approach

블럭 방법에 근거한 영상의 적응적 잡음제거 알고리즘

  • Kim, Yeong-Hwa (Department of Applied Statistics, Chung-Ang University)
  • 김영화 (중앙대학교 응용통계학과)
  • Received : 2011.10.21
  • Accepted : 2012.01.16
  • Published : 2012.03.31

Abstract

Noise reduction is an important issue in the field of image processing because image noise worsens the quality of the input image. The basic difficulty is that the noise and the signal are not easy to distinguish. Simple moothing is one of the most basic and important procedures to remove the noise, however, it does not consider the level of noise. This method effectively reduces the noise but the feature area is simultaneously blurred. This paper considers the block approach to detect noise and image features of the input image so that noise reduction could be adaptively applied. Simulation results show that the proposed algorithm improves the overall quality of the image by removing the noise according to the noise level.

Acknowledgement

Supported by : 한국연구재단

References

  1. Chan, R. H., Ho, C. W. and Nikolova, M. (2005). Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regulation, IEEE Transactions on Image Processing, 14, 1479-1485. https://doi.org/10.1109/TIP.2005.852196
  2. Eng, H. L. and Ma, K. K. (2001). Noise adaptive soft-switching median filter, IEEE Transactions on Image Processing, 10, 242-251. https://doi.org/10.1109/83.902289
  3. Hartley, H. O. (1950a). The use of range in analysis of variance, Biometrika, 37, 271-280. https://doi.org/10.1093/biomet/37.3-4.271
  4. Hartley, H. O. (1950b). The maximum F-ratio as a short cut test for heterogeneity of variance, Biometrika, 37, 308-312.
  5. Hwang, H. and Haddad, R. A. (1995). Adaptive median filters: New algorithms and results, IEEE Transactions on Image Processing, 4, 499-502. https://doi.org/10.1109/83.370679
  6. Kim, Y. H. and Lee, J. (2005). Image feature and noise detection based on statistical independent tests and their applications in image processing, IEEE Transactions on Consumer Electronics, 51, 1367-1378. https://doi.org/10.1109/TCE.2005.1561869
  7. Kim, Y. H. and Nam, J. (2007). Image feature detection and contrast enhancement algorithms based on statistical tests, Journal of the Korean Data & Information Science Society, 18, 385-399.
  8. Kim, Y. H. and Nam, J. (2008). Deinterlacing algorithm based on statistical tests, Journal of the Korean Data & Information Science Society, 19, 723-734.
  9. Kim, Y. H. and Nam, J. (2009). Statistical algorithm and application for the noise variance estimation, Journal of the Korean Data & Information Science Society, 20, 869-878.
  10. Lee, J., Kim, Y. H. and Nam, J. (2007). Adaptive noise reduction algorithms based on statistical hypotheses tests, IEEE Transactions on Consumer Electronics, 54, 1406-1414. https://doi.org/10.1109/TCE.2008.4637634
  11. Pitas, I. and Venetsanopoulos, A. (1990). Nonlinear Digital Filters: Principles and Applications, Kluwer, Boston, MA.
  12. Zhang, S. and Karim, M. A. (2002). A new impulse detector for switching median filters, IEEE Signal Processing Letter, 9, 360-363. https://doi.org/10.1109/LSP.2002.805310