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Motion-compensated neural filters for video noise reduction

Time-sequential imagery can be acquired by film-based motion camera or electronic video cameras. In this case, there are several factors related to imaging sensor limitations that contribute to the graininess of resulting images. Further, in the case of image sequence compression, random noise increases the entropy of the image sequence and therefore hinders effective compression. Thus, filtering of time- sequential imagery for noise suppression is often a desirable preprocessing step. Some of video image filtering methods use the information about motion in video for reduction of noise. The most of them are based on 3D median or average filters, which supports are along motion trajectories. In this approach, it is difficult to design the proper structure of the 3D filter by analytic methods. The artificial neural networks can be useful tool for creating the structures of the filters. In this paper the novel neural networks approach to motion compensated temporal and spatio-temporal filtering is proposed. The multilayer perceptrons and functional-link nets are used for the 3D filtering. The spatio-temporal patterns are creating from real motion video images. the neural networks learn these patterns. The practical examples of the filtering are shown and compared with traditional motion-compensated filters.