# A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model
###### Abstract  
Super-resolution in image and video processing has been a challenge in computer vision, with its progression creating substantial societal ramifications. More specifically, video super-resolution methodologies aim to restore spatial details while upholding the temporal coherence among frames. Nevertheless, their extensive parameter counts and high demand for computational resources challenge the deployment of existing deep convolutional neural networks on mobile platforms. In response to these concerns, our research undertakes an in-depth investigation into deep convolutional neural networks and offers a lightweight model for video super-resolution, capable of reducing computational load. In this study, we bring forward a unique lightweight model for video super-resolution, the Deep Residual Recursive Network (DRRN). The model applies residual learning to stabilize the Recurrent Neural Network (RNN) training, meanwhile adopting depth-wise separable convolution to boost the efficiency of super-resolution operations. Thorough experimental evaluations reveal that our proposed model excels in computational efficiency and in generating refined and temporally consistent results for video super-resolution. Hence, this research presents a crucial stride toward applying video super-resolution strategies on devices with resource limitations. depth-separable convolution (DSC); Recursive Residual Network (RRN); Super Resolution (SR) 20232023313101652023313101652023131016520231310165202313101652023131016520231310165202310165202310165202310165202310165202310165202310  
with two entities--images and videos. Image-based SR technology focuses on amplifying the subtle details whilst improving the resolution of LR images. On the other hand, the technology for video SR must guarantee a seamless flow between successive frames while also meeting the requirements of image SR. If not, the outcome might be a disjointed video sequence, which could negatively affect the user's viewing experience. In recent years, research into SR technology has been accelerating worldwide, partially due to the advent of deep learning technology [1], which, with improved hardware computational performance and the emergence of high-resolution displays, necessitates matching resolution. Amid the swift progression of digital media and the Internet's pervasive influence, video content has established itself as an indispensable part of daily life and information dissemination. High-definition, superior-quality video presentations are integral to delivering an enhanced viewing experience and catering to user needs. However, the processing and transmission of high-resolution video present significant challenges to computational and bandwidth resources. Traditional video super-resolution technology often fails to satisfy real-time performance and resource efficiency criteria, particularly in mobile devices and real-time applications. The lightweight video super-resolution technology design caters to real-time applications and mobile devices' needs, providing superior video quality while concurrently diminishing the computational burden and bandwidth usage. Existing SR algorithms can be divided into two categories: one is traditional methods, and the other is deep-learning-based methods. Traditional SR algorithms include bilinear interpolation, bicubic interpolation, sparse representation [2], Bayesian [3], Bayesian-Motion Blur [4], and so on. The deep-learning-based method is the hottest research direction, and the SR algorithm implemented by this technology is far better than the traditional SR technology. Many image and video SR algorithms based on deep learning have been proposed, and they try to improve the SR effect from different problem perspectives, including the original data itself, network structure, loss function, and so on. The first work to apply deep learning in the field of SR is the Super Resolution Convolutional Neural Network (SRCNN) [5] algorithm, which consists of three convolutional layers.