Improved EDVR Model for Robust and Efficient Video Super-Resolution
Computer vision technologies are increasingly commonly used in daily life, and video super-resolution is gradually drawing more attention in the computer vision community. In this work, we propose an improved EDVR model to tackle the robustness and efficiency problems of the original EDVR model in video super-resolution. First, to handle the blurring situations and emphasize the effective features, we devise a preprocessing module consisting of rigid convolution sub-modules and feature enhancement sub-modules, which are flexible and effective. Second, we devise a temporal 3D convolutional fusion module, which can extract information in image frames more accurately and rapidly. Third, to better utilize the information in feature maps, we design a new reconstruction block by introducing a new channel attention approach. Moreover, we use multiple programmatic methods to accelerate the model training and inference process, making the model useful for practical applications.