Blob-based super-resolution reconstruction using iterative lanczos-hybrid regularization

Super-resolution reconstruction using the pseudo- inverse approach has been proven to obtain excellent results; however it is computationally heavy. We have shown previously that incorporating blob-based basis functions into super- resolution reconstruction can guarantee better results and save computational time, and we are able to use a lower number of low-resolution datasets for the super-resolution reconstruction. Instead of directly finding the pseudo-inverse, we use iterative Lanczos bidiagonalization algorithm, combined with projection- based Tikhonov regularization for the reconstruction. We show in this paper that by using this iterative algorithm, we are able to recover super-resolution reconstructions from larger sizes of low-resolution datasets for which we are normally not able to compute the pseudo-inverse directly due to heavy computation load.