A Theoretical Tour of Connectivity in Image Processing and Analysis
Journal of Mathematical Imaging and Vision
Impulse noise removal by a global-local noise detector and adaptive median filter
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
Multiuser interference mitigation in noncoherent UWB ranging via nonlinear filtering
EURASIP Journal on Wireless Communications and Networking
Gradient estimation using wide support operators
IEEE Transactions on Image Processing
A variational method with a noise detector for impulse noise removal
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Evolutionary image enhancement for impulsive noise reduction
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Hi-index | 0.01 |
This paper describes the definition and testing of a new type of median filter for images. The topological median filter implements some existing ideas and some new ideas on fuzzy connectedness to improve, over a conventional median filter, the extraction of edges in noise. The concept of α-connectivity is defined and used to create an algorithm for computing the degree of connectedness of a pixel to all the other pixels in an arbitrary neighborhood. The resulting connectivity map of the neighborhood effectively disconnects peaks in the neighborhood that are separated from the center pixel by a valley in the brightness topology. The median of the connectivity map is an estimate of the median of the peak or plateau to which the center pixel belongs. Unlike the conventional median filter, the topological median is relatively unaffected by disconnected features in the neighborhood of the center pixel. Four topological median filters are defined. Qualitative and statistical analyses of the four filters are presented. It is demonstrated that edge detection can be more accurate on topologically median filtered images than on conventionally median filtered images