Enhanced Noise Type Recognition Using Statistical Measures

Noises are the unwanted information in an image, so they should be removed before further processing. Existing methods consider histogram based analysis which is globally varied one. A modified statistical measured based automatic noise type recognition technique is proposed in this paper. This has 2 phases including training phase and testing phase. The key role involves deduction of noise samples using filters like wiener, lee, median and extracts the statistical measures like kurtosis and skewness from samples. Kurtosis and skewness values exhibit behavior based on noise type. By using the statistical information and trained data we can classify the type of noise. Finally the noise type is identified and corresponding filter is applied. Thus noise eliminated image would give the desirable results during further processing. Experimental results show that the method is capable of accurately classifying the types of noise.