Single and Multi-view Video Super-resolution

Video super-resolution for dual-mode cameras in single-view and mono-view scenarios is studied in this thesis. Dual-mode cameras are capable of generating high-resolution still images while shooting video sequences at low-resolution. High-resolution still images are used to form a regularization function for solving the inverse problem of super-resolution. Exploiting proposed regularization function in this thesis obviates the need for classic regularization function. Experimental results show that using proposed regularization function instead of classic regularization functions for super-resolution of single-view video leads to improved results. In this thesis, superresolution problem is divided into low-resolution frame fusion and de-blurring. A frame fusion scheme for multi-view video is proposed and performance improvement when exploiting multi-view sequence instead of single-view for frame fusion is studied. Experimental results show that information taken by a set of cameras instead of a single camera can improve super-resolution process, especially when video contains fast motions. As a side work, we applied our low-resolution multi-view frame fusion algorithm to 3D frame-compatible format resolution enhancement. Multi-view video super-resolution using high-resolution still images is performed at the decoder to prevent increasing computation complexity of the encoder. Experimental results show that this method delivers comparable compression efficiency for lower bit-rates.