Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Adaptive Noise Reduction Algorithm for an Image Based on a Bayesian Method

  • Kim, Yeong-Hwa (Department of Applied Statistics, Chung-Ang University) ;
  • Nam, Ji-Ho (Korea Institute of Oriental Medicine)
  • Received : 2012.04.30
  • Accepted : 2012.06.11
  • Published : 2012.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

Noise reduction is an important issue in the field of image processing because image noise lowers the quality of the original pure image. The basic difficulty is that the noise and the signal are not easily distinguished. Simple smoothing is the most basic and important procedure to effectively remove the noise; however, the weakness is that the feature area is simultaneously blurred. In this research, we use ways to measure the degree of noise with respect to the degree of image features and propose a Bayesian noise reduction method based on MAP (maximum a posteriori). Simulation results show that the proposed adaptive noise reduction algorithm using Bayesian MAP provides good performance regardless of the level of noise variance.

Keywords

Acknowledgement

Supported by : National Research Foundation of Korea(NRF)

References

  1. Bartlett, M. A. (1937). Properties of sufficiency and statistical tests, Proceedings of the Royal Society of London, Series A, 160, 268-282. https://doi.org/10.1098/rspa.1937.0109
  2. Kim, Y. and Nam, J. (2007). Image feature detection and contrast enhancement algorithms based on statistical test, Journal of Korean Data & Information Science Society, 18, 385-399.
  3. Kim, Y. and Nam, J. (2008). Deinterlacing algorithms based on statistical tests, Journal of the Korean Data & Information Science Society, 19, 723-734.
  4. Kim, Y. and Nam, J. (2009). Statistical algorithm and application for the noise variance estimation, Journal of the Korean Data & Information Science Society, 20, 869-878.
  5. Kim, Y. 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
  6. Lee, J., Kim, Y. and Nam, J. (2008). 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
  7. Mallat, S. G. (1989). A theory A theory for multiresolution signal decomposition: The wavelet representation, IEEE Transactions on Patttern Analysis and Machine Intelligence, 11, 674-693. https://doi.org/10.1109/34.192463
  8. Simoncelli, E. P. and Adelson, E. H. (1996). Noise removal via Bayesian wavelet coring, In Proceedings of 3rd IEEE International Conference on Image Processing, 1, 379-382.