A genetic algorithm approach to image sequence interpolation ?
Image sequence interpolation, or to obtain an up-sampled image sequence equivalently from a corresponding low-resolution image sequence, is an ill-posed inverse problem. In this study, three processing steps, namely, regularization, discretization and optimization, are used to convert the image sequence interpolation problem into a solvable optimization problem. In regularization, a fitness function combining a set of spatial and temporal performance measures for rating the quality of the interpolated (up-sampled) images is defined, which is used to convert the original ill-posed interpolation problem into a well-posed optimization problem. Discretization transforms the well-posed problem into a discrete one so that it can be solved numerically. Genetic algorithms (GAs) are used to optimize the solution in the discrete solution space using three basic operations, namely, reproduction, crossover and mutation. In the proposed approach, instead of only the spatial information within the current image frame employed in most existing methods, both the spatial and temporal information within the image sequence can be employed. Based on the experimental results obtained in this study, the interpolation results by the proposed approach are always better than those from the three existing approaches used for comparison. This shows the feasibility of the proposed approach.
genetic algorithm image sequence interpolation fitness function regularization/discretization/optimization reproduction/crossover/mutation