Paper
3 November 2005 Algorithm of image enhancement based on order morphological filtering and image entropy difference
Jian-nan Chi, Dong-shu Wang, Peng-xin Zeng, Xin-he Xu
Author Affiliations +
Proceedings Volume 6043, MIPPR 2005: SAR and Multispectral Image Processing; 60431W (2005) https://doi.org/10.1117/12.654940
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China
Abstract
According to correlative conception and properties of order morphology transformation, non-linear weighted mean value filter is constructed to remove high frequence noise such as noise of Gaussian and impulse. Then an approximated midpoint value filter which can reject uniformly distributed noise is given by improving weighted mean value filter. Based on above, a new image enhancement algorithm is proposed. Within this algorithm, weighted mean value of the image about structuring elements of different directions is calculated and used to identify edge of image; Local average value and entropy difference is applied to control enhancement coefficients. So the target and edge of image are elevated while high frequence noise of image is restrained. The comparison of average value, standard deviation and image entropy between enhanced image and its original illustrates that contrast of image is also improved.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jian-nan Chi, Dong-shu Wang, Peng-xin Zeng, and Xin-he Xu "Algorithm of image enhancement based on order morphological filtering and image entropy difference", Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 60431W (3 November 2005); https://doi.org/10.1117/12.654940
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image enhancement

Image filtering

Infrared imaging

Infrared radiation

3D image enhancement

Detection and tracking algorithms

Nonlinear filtering

Back to Top