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15 November 2007 Facet-based adaptive anisotropic diffusion for image selective smoothing
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Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 67861E (2007) https://doi.org/10.1117/12.748279
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
Abstract
In each step of anisotropic diffusion smoothing, noises must be managed to get better results. The mostly used method is Gaussian filtering. However, the standard deviation of the Gaussian filter can't be accurately obtained and it should change during the iterative process. Another problem is how to select a proper standard deviation to reducing noises while preserving edges. Actually, facet model fitting can be taken as a natural way to overcome the drawbacks mentioned above. Facet model fitting has the low-pass filtering performance adaptive to the image during evolution of diffusion; it can also achieve balanced results for noise reduction and edge preserving. Experiments show the method can preserve more edges as well as obtain higher peak signal-to-noise ratio as compared to other anisotropic diffusion based selective smoothing approaches.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Guodong Wang, Nong Sang, Luxin Yan, and Xubang Shen "Facet-based adaptive anisotropic diffusion for image selective smoothing", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67861E (15 November 2007); https://doi.org/10.1117/12.748279
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