Microscanning is the process of using multiple low resolution frames differing by small rotations and translations to constrain a mapping from a higher resolution grid to the observed frame grid. This mapping can be used to reconstruct higher resolution mages from the observed low resolution frames thereby increasing the optical system resolution without changing the hardware. To be useful under operational conditions, the microscanning algorithms must work in real-time. Presently employed registration and reconstruction algorithms use iterations that slow the process down. The goal of this project is to increase the speed of the algorithms by removing the iterations. This will be accomplished by identifying local constraints that conserve the image properties. We have already successfully employed local constraints to remove the interations from segmented adaptive optics control algorithms. To further increase the speed of the microscanning process, we will also parallelize the non-iterative image registration algorithm. We will demonstrate that the parallel computation is well suited to handle cases where the images contain moving objects. Finally, we will develop a completely new non-iterative approach to microscanning processing based on orthonormal scaled functions or wavelets as apodization functions for the detector array
