Paper
30 October 2009 Fractional differential image enhancement based on space weight
Zhonghua Wang, Qingqiang Kuang, Dengwei Wang, Jianguo Liu
Author Affiliations +
Proceedings Volume 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis; 74950E (2009) https://doi.org/10.1117/12.831413
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
The theoretical analysis shows that fractional differential can greatly improve high frequency, reinforce medium frequency and non-linearly preserve low frequency of signals, hence they could be used for edge and texture enhancement as well as smooth area preservation.In this paper, a new covering template and algorithm for fractional differential image enhancement based on space weight (Fdsw) are discussed. Firstly, pixel neighbourhood relations are acquired from adopting Caputo and Riemann-Liouville fractional differential. Secondly,covering template coefficients of space weight are constructed in accordance with pixel neighbourhood relations. Constructing fractional differential template holds isotropic characteristic, and covering template coefficients are indispensable to renormalize. Finally, covering template moves on the unprocessed image point by point, then corresponding pixel value is summed. Experiments show that the new method has excellent feedback for enhancing the textural details of rich-grained digital images.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhonghua Wang, Qingqiang Kuang, Dengwei Wang, and Jianguo Liu "Fractional differential image enhancement based on space weight", Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74950E (30 October 2009); https://doi.org/10.1117/12.831413
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image enhancement

Image processing

Digital image processing

Image filtering

Algorithms

Diffusion

Digital imaging

Back to Top