Abstract
Video super-resolution (VSR) aims to restore a high-resolution (HR) frame from a given corresponding low-resolution (LR) frame. For most VSR models, the pipeline mainly includes feature extraction module, alignment module, fusion module and reconstruction module, among them, the alignment module and the fusion module are the most important. Based on BasicVSR, we propose an enhanced bidirectional propagation network which is both effective and efficient in exploiting previous and next frames to super-resolve the current frame. In the alignment module, in order to obtain a precisely offsets for alignment, we combine optical flow with deformable convolution (DCN). In the fusion module, we propose a channel and double attention module (CDAM) to extract the important features and suppress the unimportant features in channel dimension in addition, it can capture the long-range dependencies too. The experiments demonstrate the proposed method can effectively achieve superior performance compared with state-of-the-art methods.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
