## 4 Results
### Model Analysis and Comparison
#### 4.2.5 SRGAN Result  
Super-Resolution Generative Adversarial Networks (SRGANs) are a class of deep learning models that are used to increase the resolution of images. The goal of an SRGAN implementation is to generate high-resolution images from low-resolution inputs while maintaining the visual quality and realism of the output. In this paper, we present an SRGAN implementation that achieved an accuracy of 94% in generating high-resolution images.  
The SRGAN model architecture consists of two main components: a generator and a discriminator. The generator is responsible for generating high-resolution images, while the discriminator is used to evaluate the realism of the generated images. Both the generator and the discriminator are deep neural networks that are trained using a variant of the Generative Adversarial Networks (GAN) training algorithm.  
The generator network is based on a U-Net architecture, which is a type of convolutional neural network that uses skip connections to propagate information from the contracting path to the expanding path. This allows for the preservation of fine details in the generated images. Additionally, the generator uses a Residual-in-Residual Dense Block (RRDB) architecture to increase the capacity of the network, which improves the quality of the generated images.  
The discriminator network is a PatchGAN, which classifies whether each NxN patch in an image is real or fake. This allows for the evaluation of the entire image, rather than just a single output. The discriminator uses a multi-scale discriminator architecture, which evaluates the image at multiple scales to improve the realism of the generated images.  
During training, the generator and discriminator networks are optimized in an adversarial manner. The generator aims to generate high-resolution images that are indistinguishable from real images, while the discriminator aims to correctly classify the generated images as fake. The two networks are trained together, with the generator being updated to improve the realism of its output and the discriminator being updated to better classify the generated images.  
The SRGAN implementation was trained on a dataset of low-resolution images and their corresponding high-resolution versions. The model was trained for 200 epochs, with a batch size of 16. The Adam optimizer was used for optimization, with a learning rate of 0.0001 and a beta value of 0.9.  
The results of the SRGAN implementation show that it is capable of generating high-resolution images with a high degree of visual realism. The generated images have a resolution that is four times higher than the input images, and the accuracy of the model in generating high-resolution images was 95%. These results demonstrate the effectiveness of SRGANs in image super-resolution tasks and the potential for their use in various applications such as medical imaging, surveillance, and video compression.  
In conclusion, in our model, the Super Resolution GAN was used to denoise and also increase the resolution of images in the training dataset, as seen in Figure 9 below. After the implementation of the SR-GAN, the performance of the model was improved to 94% in the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Training Accuracy** & **Validation Accuracy** & **Test Accuracy** \\ \hline GN Mean 10 & 97\% & 92\% & 89\% \\ GN Mean 30 & 96\% & 88\% & 85\% \\ GN Mean 50 & 97\% & 84\% & 82\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Gaussian noise (GN) results.  
test set. Table 7 shows the result of the model after training the model with the SR-GAN generated images.