A novel decision-based adaptive feedback median filter for high density impulse noise suppression

The qualitative performances of the digital image processing methods are degraded due to the presence of impulse noise. The conventional median filter and its advanced versions somehow manage to remove the noise from image but cannot preserve the image details. In this paper, a novel decision based adaptive feedback median filter is proposed to suppress the high density noise and preserve the details of the image. The proposed method detects the corrupted or noisy pixels by analyzing the neighbours in a decisive manner, which is a challenging task for the different types of images and noise. It predicts a local threshold by analyzing the neighbours to decide the adaptive nature of the feedback median filter. The feedback mechanism is adapted to enhance the qualitative results. Various types of images and noise densities have been used to evaluate the performance of the proposed method. The qualitative and quantitative performances have been measured in terms of Peak Signal-to-Noise Ratio, Image Enhancement Factor and Structural Similarity Index. The experimental results show that the qualitative and quantitative performances are superior over existing methods and the computational time is comparable as well.