# LightVSR: A Lightweight Video Super-Resolution Model with Multi-Scale Feature Aggregation
## 2 Related Work  
There have been many research studies on the study of video super-resolution models. SRCNN [11] was the first that applies a convolutional neural network to extract features from low-resolution images to reconstruct high-resolution ones. However, SRCNN only focuses on single-frame processing and does not consider temporal information in video sequences. To address this issue, EDVR [12] and MuCan [13] models were proposed to employ a sliding window for local feature extraction to take temporal information into account. The EDVR model employs the Pyramid, Cascading, and Deformable Convolutions [14] approach to align feature maps across different levels and incorporates temporal and spatial aggregation technology to comprehensively utilize temporal information within video sequences. The MuCan model considers multiple corresponding candidates for each pixel to mitigate reconstruction quality issues stemming from motion estimation errors to improve the accuracy of video super-resolution. Figure 1: Overview of LightVSR networks. In another branch of VSR research, optical flow is utilized to precisely align and merge temporal information from consecutive frames, capturing fine motion details and enhancing the coherence of reconstructed frames in dynamic scenes. For instance, the FRVSR [15] model leverages temporal correlations between frames by introducing unidirectional optical flow to enhance video super-resolution quality. TecoGAN [16] model generates highly realistic video outputs by incorporating generative adversarial networks [17]. To further improve the accuracy and robustness of feature alignment, the BasicVSR [18] model integrates bidirectional optical flow, considering both forward and backward motion information, to fully extract inter-frame information. The SwinIR [19] model, on the other hand, combines the transformer architecture to enhance the capture of global context through a global attention mechanism. StableVSR [20] was the first model to employ diffusion models within a generative paradigm for video super-resolution (VSR), which utilizes a temporal texture guidance method to incorporate detail-rich and spatially aligned texture information synthesized from adjacent frames to enhance the perceptual quality of images. [PERSON] et al. proposed an alignment method based on implicit resampling, where features and estimated motion are jointly learned through coordinate networks, with alignment implicitly achieved through window-based attention [21]. [PERSON] et al. made the first attempt to tackle the non-trivial problem of learning implicit neural representations from events and RGB frames for VSR at random scales, effectively leveraging the high temporal resolution of events to complement RGB frames through spatial-temporal fusion [22]. However, the aforementioned VSR models generally require substantial computational loads and a large number of parameters, limiting their application on resource-constrained devices such as mobile phones, since these devices typically have limited computational resources and storage space. There is also some research that focused on lightweight VSR models. [PERSON] et al. proposed the local omniscient VSR and global omniscient VSR models based on the iterative, recurrent, and hybrid omniscient framework by leveraging long-term and short-term memory frames from the past, present, and future, as well as estimated hidden states, to achieve efficient real-time video super-resolution [23]. However, their training process is unstable due to the utilization of mixed-precision training methods. PP-MSVSR [24] employs a lightweight, multi-stage sliding-window framework to effectively utilize temporal information between frames. However, its reliance on multi-stage alignment may degrade the efficiency of video super-resolution in highly dynamic scenes. FDDCC-VSR [25] combines a deformable convolutional network [14], a lightweight spatial attention module, and improved bicubic interpolation to capture complex motion information and enhance high-frequency details.