Using LM Artificial Neural Networks and η-Closest-Pixels for Impulsive Noise Suppression from Highly Corrupted Images

摘要

In this paper, a new filter, η - LM, which is based on Leven-berg-Marquardt Artificial Neural Networks, is proposed for the impulsive noise suppression from highly distorted images. The η - LM uses Anderson-Darling goodness-of-fit test in order to find corrupted pixels more accurately. The extensive simulation results show that the proposed filter achieves a superior performance to the other filters mentioned in this paper in the cases of being effective in detail preservation and noise suppression, especially when the noise density is very high.