標題: Adaptive symmetric mean filter: A new noise-reduction approach based on the slope facet model
作者: Huang, HC
Chen, CM
Wang, SD
Lu, HHS
統計學研究所
Institute of Statistics
公開日期: 10-Oct-2001
摘要: Two new noise-reduction algorithms, namely, the adaptive symmetric mean filter (ASMF) and the hybrid filter, are presented in this paper. The idea of the ASMF is to find the largest symmetric region on a slope facet by incorporation of the gradient similarity criterion and the symmetry constraint into region growing. The gradient similarity criterion allows more pixels to be included for a statistically better estimation, whereas the symmetry constraint promises an unbiased estimate if the noise is completely removed. The hybrid filter combines the advantages of the ASMF, the double-window modified-trimmed mean filter, and the adaptive mean filter to optimize noise reduction on the step and the ramp edges. The experimental results have shown the ASMF and the hybrid filter are superior to three conventional filters for the synthetic and the natural images in terms of the root-mean-squared error, the root-mean-squared difference of gradient, and the visual presentation. (C) 2001 Optical Society of America.
URI: http://hdl.handle.net/11536/29344
ISSN: 0003-6935
期刊: APPLIED OPTICS
Volume: 40
Issue: 29
起始頁: 5192
結束頁: 5205
Appears in Collections:Articles


Files in This Item:

  1. 000171406500007.pdf