# LightVSR: A Lightweight Video Super-Resolution Model with Multi-Scale Feature Aggregation
## 1 Introduction  
With the rapid development of information technology and the widespread application of digital media, video has become an important medium for information dissemination, entertainment, and social interaction. However, in practical applications, limitations in aspects such as acquisition devices, transmission bandwidth, and storage capacity often result in reduced video resolution, which in turn degrades user viewing experience and the efficiency of video data utilization. To address these challenges, video super-resolution (VSR) technology has become an important research direction in the fields of computer vision and image processing.  
Video super-resolution is a technique that converts low-resolution video into high-resolution video through advanced signal processing and image processing methods [1, 2, 3, 4]. Since VSR can significantly enhance the visual quality of videos, it has broad application potential in various fields such as video surveillance, medical imaging, remote sensing, and the entertainment industry [5, 6, 7]. Although many works have proposed advanced VSR models, these models often require substantial computational resources to reconstruct high-resolution videos, making it difficult to deploy VSR models effectively on hardware platforms with limited computational resources. To solve this problem, there is an urgent need to design lightweight VSR models that can maintain high reconstruction quality while significantly reducing the computational complexity and inference time of VSR models, thereby improving their adaptability on resource-constrained hardware platforms.  
In this paper, we propose a lightweight video super-resolution model (LightVSR) to enhance reconstruction quality while optimizing computational efficiency. As illustrated in Figure 1, LightVSR first utilizes the lightweight SPYNET model [8] for optical flow estimation to capture the motion information in the temporal dimension between the current frame and its neighboring frames. Then we design a novel lightweight feature aggregation module to fuse motion information from preceding and subsequent video frames via cross-layer shortcut connections to achieve multi-scale feature fusion, so as to facilitate finer detail restoration while maintaining a small number of model parameters. Subsequently, LightVSR employs a dual-layer pixel shuffle module [9] to perform the upsampling process, followed by a channel reduction module to obtain RGB color information. Finally, LightVSR combines low-resolution input frames up-sampled by bicubic interpolation [10] and high-resolution feature maps generated by the channel reduction module to produce the final high-resolution output frames.  
The main contributions of this paper are as follows:  
1. We propose a lightweight VSR model that utilizes bidirectional optical flow for effective motion compensation, enabling efficient video frame reconstruction even on low-power devices.
2. We design head-tail convolution, a novel convolutional operation that significantly reduces redundant computations while ensuring the effective representation of critical features across the entire feature tensor.
3. We design a multi-input attention mechanism that integrates features from multiple sources by utilizing channel-wise attention to achieve comprehensive and efficient feature fusion.
4. Combined head-tail convolution and multi-input attention mechanisms, we further design a feature aggregation module that leverages cross-layer shortcut connections to enhance computational efficiency while maintaining model performance.