Algorithm based on super low resolution video image processing

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In the present video image processing community, people tend to utilize computational resources as much as possible rather than try to reduce the computational complexity of a given motion estimation algorithm for realizing real-time image processing. In our research, insects with very simple neural systems, capable of controlling their flight maneuvers in real time are focused and their computational models, so called an elementary motion detector (EMD) and its derivative an elaborated Reichardt detector (ERD), are theoretically analyzed for developing an motion estimation algorithm of low computational complexity to which very low resolution video images are fed in real time.In our research, it has been found that (i) estimation results are a function of filter parameters included in these models, (ii) estimation accuracy can be improved greatly by optimizing the filter parameters, and (iii) such an optimization can be done in a combination of analytical and numerical procedures. The performance of our developed algorithms based on the above numerical modes was examined by implementing them in software programs and hardware resources. The algorithms can estimate motion successfully, although in certain limited conditions, in real time utilizing very low resolution video image sequences.