Video Super-Resolution Based on Inter-Frame Information Utilization for Intelligent Transportation

Abstract

Intelligent transportation infrastructure is essential to intelligent transportation system (ITS). With the continuous development of Internet of Things (IoT) technology, remote monitoring has become a critical part of ITS. However, due to the limitations of network transmission, production cost, and other factors, some video monitoring can obtain only low-resolution (LR) video. LR video features are seriously lost, thus affecting the performance of ITS. In this paper, based on the research of existing super-resolution algorithms, we focus on improving the reconstruction quality of video frame sequences by aiming at the insufficient utilization of inter-frame information and low reconstruction quality of existing video super-resolution algorithms. This paper proposes a video super-resolution algorithm based on inter-frame information utilization, which can effectively improve the performance of ITS. First, a novel U-shaped feature extractor is designed to fully extract the feature expression of video frame sequences. Second, a deformable inter-frame alignment module based on residual learning is constructed to make the inter-frame alignment more accurate and thus promote the mutual utilization of inter-frame information. Finally, an up and down sampling residual block is proposed to extract features that better match the upsampling reconstruction requirements. The experimental results show that the method has better reconstruction quality for monitoring video and is advanced and applicable compared to mainstream video oversampling methods.