DOI QR코드

DOI QR Code

A de-noising method based on connectivity strength between two adjacent pixels

  • Ye, Chul-Soo (Department of Ubiquitous IT, Far East University)
  • Received : 2015.01.13
  • Accepted : 2015.02.26
  • Published : 2015.02.28

Abstract

The essential idea of de-noising is referring to neighboring pixels of a center pixel to be updated. Conventional adaptive de-noising filters use local statistics, i.e., mean and variance, of neighboring pixels including the center pixel. The drawback of adaptive de-noising filters is that their performance becomes low when edges are contained in neighboring pixels, while anisotropic diffusion de-noising filters remove adaptively noises and preserve edges considering intensity difference between neighboring pixel and the center pixel. The anisotropic diffusion de-noising filters, however, use only intensity difference between neighboring pixels and the center pixel, i.e., local statistics of neighboring pixels and the center pixel are not considered. We propose a new connectivity function of two adjacent pixels using statistics of neighboring pixels and apply connectivity function to diffusion coefficient. Experimental results using an aerial image corrupted by uniform and Gaussian noises showed that the proposed algorithm removed more efficiently noises than conventional diffusion filter and median filter.

Keywords

References

  1. Alvarez, L., P.-L. Lions, and J.-M. Morel, 1992. Image selective smoothing and edge detection by nonlinear diffusion II, SIAM Numer. Anal., 29(3): 845-866. https://doi.org/10.1137/0729052
  2. El-Fallah, A. and G. Ford, 1997. Mean curvature evolution and surface area scaling in image filtering, IEEE Trans. Image Processing, 6(5):750-753. https://doi.org/10.1109/83.568931
  3. Frost, V.S., J.A. Stiles, K.S. Shanmugan, and J.C. Holtzman, 1982. A model for radar image and its application to adaptive digital filtering of multiplicative noise, IEEE Trans. on Pattern Analysis and Machine Intelligence, 4(2): 157-166.
  4. Ham, B., D. Min, and K. Sohn, 2013. Revisiting the relationship between adaptive smoothing and anisotropic diffusion with modified filters, IEEE Trans. on Image Processing, 22(3): 1096-1107. https://doi.org/10.1109/TIP.2012.2226904
  5. Kuan, D.T., A.A. Sawchuk, T.C. Strand, and P. Chavel, 1985. Adaptive noise smoothing filter for images with signal dependent noise, IEEE Trans. on Pattern Analysis and Machine Intelligence, 7(2): 165-177.
  6. Lee, J.S., 1980. Digital image enhancement and noise filtering by use of local statistics, IEEE Trans. on Pattern Analysis and Machine Intelligence, 2(2): 165-168.
  7. Lee, J.S., 1983. A simple speckle smoothing algorithm for synthetic aperture radar images, IEEE Trans. on System, Man and Cybernetics, SMC-13(1): 85-89. https://doi.org/10.1109/TSMC.1983.6313036
  8. Perona, P. and J. Malik, 1990. Scale space and edge detection using anisotropic diffusion, IEEE Trans. on Pattern Analysis and Machine Intelligence, 12(7): 629-639. https://doi.org/10.1109/34.56205
  9. Wang, Y., R. Niu, and X. Yu, 2010. Anisotropic diffusion for hyperspectral imagery enhancement, IEEE Sensors Journal, 10(3):469-477. https://doi.org/10.1109/JSEN.2009.2037800
  10. Ye, C.S., 2009. Speckle noise removal by rank-ordered differences diffusion filter, Korean Journal of Remote Sensing, 25(1): 21-30 (In Korean with English abstract). https://doi.org/10.7780/kjrs.2009.25.1.21
  11. Ye, C.S. and K.H. Lee, 2001. Anisotropic diffusion for building segmentation from aerial imagery, Proc. of International Symposium on Remote Sensing, EMSEA and KSRS, Seogwipo, Korea, Oct. 31-Nov.2, pp. 599-604.

Cited by

  1. Water body extraction using block-based image partitioning and extension of water body boundaries vol.32, pp.5, 2016, https://doi.org/10.7780/kjrs.2016.32.5.6