| Title |
Impulse Noise Detection Using Self-Organizing Neural Network and Its Application to Selective Median Filtering |
| Authors |
이종호(Lee Chong Ho) ; 동성수(Dong Sung Soo) ; 위재우(Wee Jae Woo) ; 송승민(Song Seung Min) |
| Keywords |
Median Filter ; Impulse Noise Detection ; Pattern Classification ; Self-Organizing Neural Network(SONN) |
| Abstract |
Preserving image features, edges and details in the process of impulsive noise filtering is an important problem. To avoid image blurring, only corrupted pixels must be filtered. In this paper, we propose an effective impulse noise detection method using Self-Organizing Neural Network(SONN) which applies median filter selectively for removing random-valued impulse noises while preserving image features, edges and details. Using a 3×3 window, we obtain useful local features with which impulse noise patterns are classified. SONN is trained with sample image patterns and each pixel pattern is classified by its local information in the image. The results of the experiments with various images which are the noise range of 5-15 % show that our method performs better than other methods which use multiple threshold values for impulse noise detection. |