Flow Based Video Super-Resolution with Spatio-temporal Patch Similarity

The goal of super-resolution is to fuse several low-resolution images of the same scene into a single one with increased resolution. The classical formulation assumes that the super-resolved image is related to the low-resolution frames by warping, convolution and subsampling. Algorithms divide into those using explicit registration and those avoiding it. The first ones combine for each pixel the information in its estimated trajectory. The second ones exploit both spatial and temporal redundancy. We propose to combine both ideas, making use of optical flow and exploiting spatio-temporal redundancy with patch-based techniques. The proposed non-linear filtering takes into account patch similarities, automatically correcting the flow inaccuracies and avoiding the need of occlusion detection. Total variation and nonlocal regularization are used for the deconvolution stage. The experimental results demonstrate the state-of-the-art performance of the proposed approach.