Recursive high-resolution reconstruction of blurred multiframe images

An approach to obtain high-resolution image reconstructions from low-resolution, blurred, and noisy multiple-input frames is presented. A recursive least-square approach with iterative regularization is developed in the DFT domain. When the input frames are processed recursively, the reconstruction does not converge in general due to the measurement noise and ill-conditioned nature of the deblurring. Through the iterative update of the regularization function and the proper choice of the regularization parameter, good high-resolution reconstructions are obtained. This provides an unified approach for deblurring, noise removal, and high resolution reconstruction of low-resolution, blurred, and noisy input frames. The proposed recursive algorithm minimizes the required computations and provides the parallel computation structure since the reconstruction is done independently for each DFT element. Computer simulations demonstrate the performance of the algorithm.