## 3 Materials and Methods
### Video Super Resolution  
To obtain high-resolution videos, this study use a deep-learning-based video super-resolution approach as an effective strategy for overcoming technical limitations associated with low-quality videos: particularly, poor lighting and low spatial resolution. The proposed implementation employs GLEAN [56]: a framework that uses a Generative Adversarial Network (GAN) as a latent bank to extract rich and diverse priors from a pre-trained GAN model. Unlike traditional GAN-based methods, which involve adversarial loss and costly optimization through GAN inversion, our approach uses a single forward pass to generate high-resolution images.  
To overcome poor lighting and low spatial resolution, a StyleGAN [57] is used in our implementation. The StyleGAN, fine-tuned on a dataset of surveillance videos with both low- and high-resolution representations of each frame, serves as a pre-trained latent feature bank. This latent feature bank functions similarly to a dictionary, but its distinct advantage is its nearly infinite feature bank, which provides superior priors for generating realistic high-resolution videos. Furthermore, our encoder-bank-decoder formulation, illustrated in Figures 2 and 3, is crucial for obtaining super-resolution images. Notably, the encoder accepts an input resolution of \(40\times 30\) pixels and outputs \(320\times 240\) pixels, demonstrating its ability to handle low-spatial-resolution scenarios. The latent feature bank,which is powered by the pre-trained StyleGAN, ensures that the generated high-resolution videos retain realism and fidelity even in challenging lighting conditions.  
In order to obtain high-resolution videos apart from interpolation-based upscaling techniques, deep-learning-based video super resolution is an attractive approach. Generative Adversarial Networks (GANs) built using neural networks have shown excellent performance in video generation, enhancement, and super resolution, among other tasks. GLEAN [56] is an approach that uses a GAN-based model as a latent bank to obtain rich and diverse priors from pre-trained GAN. Unlike existing GAN-based approaches that generate realistic outputs through adversarial loss and the use of expensive optimization through GAN inversion, this approach uses a single forward pass to generate a high-resolution image. In this implementation, we used a StyleGAN [57] and fine-tuned it on a dataset of surveillance videos containing low-resolution and high-resolution representations of each frame.  
Super-resolution images are obtained from low-resolution images by using an encoder-bank-decoder formulation. The latent features bank acts like a dictionary as in traditional approaches but differs in the sense that dictionaries contain a finite feature bank, whereas a GAN contains a practically infinite feature bank, making it a superior prior. The architecture of encoder-bank-decoder used in this implementation is provided in Figure 3. Note that the encoder accepts an input resolution of \(40\times 30\) pixels and provides an output of \(320\times 240\) pixels. The bank is a pre-trained StyleGAN that acts as a latent feature bank to provide realistic high-resolution videos.  
Figure 3: Video super-resolution framework (encoder–bank–decoder) based on pre-trained StyleGAN.  
Figure 2: Encoder–bank–decoder representation [58].