## 3 Experiment and Discussion
### Comparative Experiments  
To verify whether DSpix2 pix can generate higher-quality images compared to other generative networks, we used Sentinel-2 data from Birmingham and its surrounding areas in the UK, collected between April and August 2022, with cloud removal performed using the Cloudy algorithm for synthesis [2]. The original data can be obtained from the ESA (European Space Agency) website. The dataset was created consisting of 2244 image patches of 256 \(\times\) 256 pixels, with 2000 patches used as the training set and 224 patches as the validation set. Low-resolution images were created using bilinear interpolation at 2\(\times\), 4\(\times\) and 8\(\times\) downscaling factors. In the experiment, we selected the following networks for comparison experiments: UNIT, which requires no paired training data, achieves effective image translation between different domains through a shared latent space, and can generate multi-modal outputs; DRIT, which features disentangled representations and separates the content and style of images to achieve flexible and diverse image translations, making it suitable for unpaired data; CycleGAN, which performs bidirectional image translation with cycle consistency loss, ensuring content consistency without paired data [35]; pix2 pix, which uses conditional GANs, generates high-resolution outputs given specific inputs, and is applicable to various image translation tasks such as image restoration, coloring, and segmentation; and SRGAN, focusing on super-resolution tasks, uses perceptual loss to generate high-resolution, detail-rich images. The upward arrow indicates that a higher metric value corresponds to better performance, whereas a downward arrow indicates that a lower metric value corresponds to better performance. The results are shown in Tables 1-3. The evaluation metrics used are as follows: RMSE (Root Mean Squared Error), PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index) [36], ERGAS (Erreur Relative Globale Adimensionnelle de Synthese), LPIPS (Learned Perceptual Image Patch Similarity) [37], and SAM (Spectral Angle Mapper) [38]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Downsamples 2\(\times\)**} \\ \cline{2-7}  & **RMSE**\(\downarrow\) & **PSNR**\(\uparrow\) & **SSMI**\(\uparrow\) & **ERGAS**\(\downarrow\) & **LPIPS**\(\downarrow\) & **SAM**\(\downarrow\) \\ \hline Bicubic & 53.05 & 23.49 & 0.58 & **2.97** & 0.65 & 14.47 \\ pix2 pix & 43.05 & 25.41 & 0.76 & 3.11 & 0.30 & 15.10 \\ CycleGAN & 168.84 & 13.19 & 0.26 & 3.29 & 0.56 & 14.71 \\ DRIT & 186.95 & 12.51 & 0.25 & 3.30 & 0.59 & **14.40** \\ UNIT & 49.37 & 24.03 & 0.70 & 3.05 & 0.20 & 14.98 \\ SRGAN & 51.10 & 24.48 & 0.63 & 3.13 & 0.35 & 15.46 \\ DSpix2 pix & **35.87** & **26.56** & **0.84** & 3.11 & **0.18** & 15.08 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of generation effects: DSpix2 pix vs. other networks in 2\(\times\) super-resolution tasks. Due to differences in network structure design and application scenarios, SRGAN performed excessively poorly in the 8\(\times\) super-resolution task and was therefore excluded from the statistics. From these tables, we find that DSpix2 pix achieved good results in metrics such as RMSE, PSNR, SSIM, and LPIPS, which are commonly used to evaluate visual quality. Among all the compared methods, DSpix2 pix generated images with the smallest RMSE, indicating that the images generated by this method are the closest to the original images, demonstrating its high accuracy in image reconstruction. It also achieved a higher PSNR, an important metric for evaluating image quality. A higher PSNR value indicates less noise, making the generated images clearer and containing fewer artifacts. Additionally, DSpix2 pix performed better on the SSIM metric, which measures the structural similarity between images.