Super-resolution Fusion using Adaptive Normalized Averaging

A fast method for super-resolution (SR) reconstruction from low resolution (LR) frames with known registration is proposed. The irregular LR samples are incorporated into the SR grid by stamping into 4-nearest neighbors with position certainties. The signal certainty reflects the errors in the LR pixels’ positions (computed by cross-correlation or optic flow) and their intensities. Adaptive normalized averaging is used in the fusion stage to enhance local linear structure and minimize further blurring. The local structure descriptors including orientation, anisotropy and curvature are computed directly on the SR grid and used as steering parameters for the fusion. The optimum scale for local fusion is achieved by a sample density transform, which is also presented for the first time in this paper.