Multi-Frame Super Resolution With Deep Residual Learning On Flow Registered Non-Integer Pixel Images

Super-Resolution (SR) of low-quality images is an important topic of research in image processing and computer vision field. Using multi-frame, super-resolution algorithm can reconstruct high-resolution images by incorporating the information of the subsequent images. Most of the super-resolution techniques for multi-frames either use a more traditional or mathematical approach or deep learning based approach with optical flow in consideration. In this paper, we develop a way to combine the optical flow enabled sub-pixel registration method for mapping into the high-resolution grid and a deep residual learning approach for restoring features with noise removal. The results exhibit a significant gain over the state of art methods and the bi-cubic interpolation method.