To the best of our knowledge, the existing deep-learning-based Video Super-Resolution (VSR) methods exclusively make use of videos produced by the Image Signal Processor (ISP) of the camera system as inputs. Such methods are 1) inherently suboptimal …
Video super-resolution aims at generating a high-resolution video from its low-resolution counterpart. With the rapid rise of deep learning, many recently proposed video super-resolution methods use convolutional neural networks in conjunction with …
Conventional Convolutional Neural Network (CNN) based video super-resolution (VSR) methods heavily depend on explicit motion compensation. Input frames are warped according to flow-like information to eliminate inter-frame differences. These methods …