High density salt and pepper noise removal through decision based partial trimmed global mean filter

Denoising is an important problem in signal and image processing. In this paper a denoising algorithm is proposed which eliminates salt and pepper noise from digital images that are highly corrupted by salt and pepper noise. Several methods have been introduced to remove fixed value impulse noise (salt and pepper noise) from digital images such as median filter (MF), adaptive median filter (AMF), Decision based algorithms (DBA) etc. Many of these algorithms fail while removing the noise at high density and do not preserve fine details of the image. The proposed algorithm (PA) shows better results than existing filtering methods. The algorithm tested and compared for Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF) with different existing methods.