Removal of Mixed Impulse noise and Gaussian noise using genetic programming
R. P. Aher, K.C. Jodhanle · 2012
In this paper, we have put forward a nonlinear filtering method for removing mixed Impulse and Gaussian noise, based on the two step switching scheme. The switching scheme uses two cascaded detectors for detecting the noise and two corresponding estimators which effectively and efficiently filters the noise from the image. A supervised learning algorithm, Genetic programming, is employed for building the two detectors with complementary characteristics. Most of the noisy pixels are identified by the first detector. The remaining noises are searched by the second detector, which is usually hidden in image details or with amplitudes close to its local neighborhood. Both the detectors designed are based on the robust estimators of location and scale i.e. Median and Median Absolute Deviation (MAD). Unlike many filters which are specialized only for a particular noise model, the proposed filters in this paper are capable of effectively suppressing all kinds of Impulse and Gaussian noise. The proposed two-step Genetic Programming filters removes impulse and Gaussian noise very efficiently, and also preserves the image details.