Image De-Noising Algorithm Based on Intersection Cortical Model and Median Filter
Estela Ortiz, Manuel Mejía-Lavalle, Dante Mújica‐Vargas, Gerardo Reyes · 2015
In order to reduce the noise effect in gray scale images, an algorithm that combines a Pulse-Coupled Neural Network (PCNN) and the median estimator is proposed to remove Salt and Pepper noise. The proposed algorithm is based on a simplified PCNN called Intersection Cortical Model (ICM). By using the output images of ICM, we can ratify that the pixel position corresponds to Salt and Pepper noise. Then, a selective median filter is used for suppressing the Salt and Pepper on noisy pixels. The performance of the proposed method is tested by simulating different impulsive noise densities. Simulation results show that method's effectiveness is bigger than conventional median filter noise suppression, the results are represented by the parameter Peak Signal to Noise Ratio (PSNR).