A novel image smoothing filter using membership function.
This paper presents a new class of image noise smoothing algorithms utilizing the membership information of the neighboring pixels. The basic idea of this method is to compute the smoothed output using neighboring pixels from the same cluster to avoid image blurring. A fuzzy c-means algorithm is first applied to the image to separate the image pixels into a certain number of clusters. A membership function is defined as the probability that a pixel belongs to a cluster. The proposed method uses this membership function as a weight to calculate the weighted sum of the pixel values from its neighboring pixels. The results of the application of this algorithm to various images show that it can smooth images with edge enhancement. The smoothness of the resultant images can be controlled by the cluster number and window size.
Chen
Tzong-Jer
TJ
Department of Medical Imaging Technology, Shu-Zen College of Medicine and Management, Luju Shiang, Kaohsiung, 82144, Taiwan.
Chuang
Keh-Shih
KS
Chen
Sharon
S
Lu
Jeng-Chang
JC
Shiao
Ya-Hui
YH
D000465:Algorithms; D016477:Artifacts; D003952:Diagnostic Imaging; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D012815:Signal Processing, Computer-Assisted