High-frequency refinement for sharper video super-resolution

A video super-resolution technique is expected to generate a 'sharp' upsampled video. The sharpness in the generated video comes from the precise prediction of the high-frequency details (e.g. object edges). Thus high-frequency prediction becomes a vital sub-problem of the super-resolution task. To generate a sharp-upsampled video, this paper proposes an upsampling network architecture 'HFR-Net' that works on the principle of 'explicit refinement and fusion of high-frequency details'. To implement this principle and to train HFR-Net, a novel technique named 2-phase progressive-retrogressive training is being proposed. Additionally, a method called dual motion warping is also being introduced to preprocess the videos that have varying motion intensities (slow and fast). Results on multiple video datasets demonstrate the improved performance of our approach over the current state-of-the-art.