Many methods have been proposed throughout the years for solving multiframe video restoration problems. With that, well-established restoration methods exist only for situations in which all sources of blur and degradation are known or easily predicted. When some of the parameters are unknown the problem becomes difficult. The presented research addresses this challenge. At first motion estimation techniques are evaluated. Based on the evaluation of these methods, an improvement, through numerical exact derivation, is suggested. Accurate evaluation of the motion field allows a reliable video stabilization and improving frames resolution. The super-resolution (SR) method is then improved by the theory of discrete signal reconstruction from sparse data. Additionally, SR’s potential and limitations are sought. Finally, the developed stabilization and SR methods are utilized for providing means for reasoning the scene observed. The results presented are of both theoretical and practical interest and offer new efficient tools for substantial improvement of infrastructure of vision- based systems in general and of intelligent transportation systems in particular.

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