Geodesic
Adaptive Wavelet Transform Based on Artificial Fish Swarm Optimization and Fuzzy C-means Method for Noisy Image Segmentation
Computer Science and Information Systems, Tome 19 (2022) no. 3.

Voir la notice de l'article provenant de la source Computer Science and Information Systems website

Aiming at the problem that traditional fuzzy C-means (FCM) clustering algorithm is susceptible to noise in processing noisy images, a noisy image segmentation method based on FCM wavelet domain feature enhancement is proposed. Firstly, the noise image is decomposed by two-dimensional wavelet. Secondly, the edge enhancement of the approximate coefficient is carried out, and the artificial fish swarm (AFS) optimization algorithm is used to process the threshold value of the detail coefficient, and the processed coefficient is reconstructed by wavelet transform. Finally, the reconstructed image is segmented by FCM algorithm. Five typical gray-scale images are selected by adding Gaussian noise and Salt pepper noise, respectively, and segmented by various methods. The peak signal-to-noise ratio (PSNR) and error rate (MR) of segmented images are used as performance indexes. Experimental results show that compared with traditional FCM clustering algorithm segmentation method, particle swarm optimization (PSO) segmentation method and other methods, the indexes of image segmentation by the proposed method is greatly improved. It can be seen that the proposed segmentation method retains the texture information of image edge well, and its anti-noise performance and segmentation performance are improved.
Keywords: FCM, artificial fish swarm optimization, wavelet transform, noisy image segmentation 
@article{CSIS_2022_19_3_a18,
     author = {Rui Yang and Dahai Li},
     title = {Adaptive {Wavelet} {Transform} {Based} on {Artificial} {Fish} {Swarm} {Optimization} and {Fuzzy} {C-means} {Method} for {Noisy} {Image} {Segmentation}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {19},
     number = {3},
     year = {2022},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2022_19_3_a18/}
}
TY  - JOUR
AU  - Rui Yang
AU  - Dahai Li
TI  - Adaptive Wavelet Transform Based on Artificial Fish Swarm Optimization and Fuzzy C-means Method for Noisy Image Segmentation
JO  - Computer Science and Information Systems
PY  - 2022
VL  - 19
IS  - 3
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/CSIS_2022_19_3_a18/
ID  - CSIS_2022_19_3_a18
ER  - 
%0 Journal Article
%A Rui Yang
%A Dahai Li
%T Adaptive Wavelet Transform Based on Artificial Fish Swarm Optimization and Fuzzy C-means Method for Noisy Image Segmentation
%J Computer Science and Information Systems
%D 2022
%V 19
%N 3
%I mathdoc
%U http://geodesic.mathdoc.fr/item/CSIS_2022_19_3_a18/
%F CSIS_2022_19_3_a18
Rui Yang; Dahai Li. Adaptive Wavelet Transform Based on Artificial Fish Swarm Optimization and Fuzzy C-means Method for Noisy Image Segmentation. Computer Science and Information Systems, Tome 19 (2022) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2022_19_3_a18/