Title :
A self-constructing type-2 fuzzy neural network for impulse noise removal in digital images
Author :
Chen-Sen Ouyang ; Po-Jen Cheng
Author_Institution :
Dept. of Inf. Eng., I-Shou Univ., Kaohsiung, Taiwan
Abstract :
We propose a self-constructing type-2 fuzzy neural network for impulse noise removal in digital images. The architecture and corresponding parameters of the network are initialized by a SVD-based self-constructing rule generation algorithm, and the initialized parameters are optimized by a hybrid learning algorithm. The trained network can be employed to detect the noisy pixels in the images corrupted by impulse noise. After that, pixels identified as noisy are processed by a median filter whereas the other pixels are retained. Compared with the other approach, experimental results have shown that our approach possesses a better detection capability of impulse noise, resulting in the more effective filtering of impulse noise.
Keywords :
fuzzy neural nets; image denoising; learning (artificial intelligence); median filters; singular value decomposition; SVD-based self-constructing rule generation algorithm; digital images; hybrid learning algorithm; image detection capability; impulse noise removal; median filter; noisy pixels; trained network; type-2 fuzzy neural network; Boats; Digital images; Educational institutions; Image restoration; Noise; Noise measurement; Training;
Conference_Titel :
Fuzzy Theory and Its Applications (iFUZZY), 2013 International Conference on
Conference_Location :
Taipei
DOI :
10.1109/iFuzzy.2013.6825454