Impulse Noise Filtering using MLMVN

In this paper, we consider how a complex-valued neural network, the multilayer neural network with multi-valued neurons (MLMVN), can be efficiently used for impulse noise filtering. It is shown that MLMVN with only a single hidden layer of neurons can restore an image corrupted by random-valued impulse noise while carefully preserving edges and boundaries. MLMVN processes overlapping patches, which to a noisy image should be broken down. A network shall be trained using a robust learning set created from patches randomly picked up from many images. Then, the trained network should be used for actual filtering. Final intensities in pixels of a resulting image are obtained by averaging the intensities over all overlapping patches. This approach becomes highly efficient for images corrupted by random impulse noise with a low corruption rate of 5-10%. It makes it possible to preserve the smallest image details very carefully and avoid the smoothing of edges. MLMVN outperforms sophisticated filters with detectors of impulse noise for a lower corruption rate and shows comparable results for a higher corruption rate.