## 4 Results  
### Experiment with Data-1  
To ascertain the efficiency of the proposed methodology, a comprehensive experimental and training study was conducted using the Data-1 dataset. As shown in Figure 6, the training loss curve of the generator reflects the dynamic trajectory of the stability of the adversarial training process. In the first 30 periods, there were significant fluctuations in losses. Subsequently, the generator quickly adapted to complex data distributions, and the fluctuations gradually decreased between 30 and 80 periods, indicating that as the generator improved its feature mapping ability, the average loss steadily decreased. After 80 epochs, the loss stabilized around 0.017 with minimal oscillation, indicating convergence. The transient fluctuations that occur during the training process are inherent to GAN training, stemming from the mutual constraint objectives of the generator and discriminator. Despite these oscillations, the gradual downward trend and attenuation amplitude demonstrate the stability of convergence, with no signs of mode collapse. As shown in Figure 7, the results of the images processed by the proposed algorithm with several comparison methods are demonstrated. Specifically, Figure 7a,b represent the original noisy image and its corresponding real image (ground truth), respectively, whereas from Figure 7c-l demonstrate different denoising methods, such as NLM, BM3D, DnCNN, SDSR-OCT, DudeNet, MDGAN, MDRGAN, DCANet, and DRANet. To quantitatively assess the quality of these images, we manually selected four signal regions and one background region in each image, and accordingly calculated the contrast-to-noise ratio (CNR) and equivalent number of views (ENL) for each region. In order to observe the retinal structural information more clearly for a more convenient quality assessment, we performed local magnification of the four signal regions. In the figure, it can be observed that the traditional NLM (Figure 7c) and BM3D (Figure 7d) denoising methods performed poorly in terms of effectiveness, especially in the inner and border regions of the retina, where the image was more severely corrupted. Compared with NLM and BM3D, DnCNN (Figure 7e) improved in scatter removal, but the scatter suppression effect was still not significant enough. SDSR-OCT (Figure 7f) and MDRGAN (Figure 7i) effectively removed scatter noise, but blocky and striped artifacts appeared in the image, which reduced the overall quality and perception of the image. MDGAN (Figure 7h) and DCANet  
Figure 6: Generator loss curve graph. (Figure 7j)), on the other hand, excessively smoothed the denoised image, which resulted in the complicated textures becoming blurred, features becoming difficult to distinguish from the background, and insufficient enhancement of retinal structures, which in turn affected the overall readability of the image. In contrast, although DudeNET (Figure 7g) and DRANet (Figure 7k) could better preserve the edge details and texture information of the image, there were still obvious scattering particles in the denoised image, which influenced the observation of the fine structures in the image. In contrast, our proposed method not only effectively suppressed the speckle noise but also preserved the edge details and texture features in the image. At the same time, the method resulted in a high contrast between the signal region and the background region, which effectively maintained the clarity and resolution of the image. As demonstrated in Table 1, there were significant differences in the performances of the different image denoising models in terms of key performance indicators. Traditional methods, such as NLM and BM3D, due to low performance in four performance aspect metrics, had limited effectiveness in processing complex images. Although BM3D showed some advantages in ENL, this stemmed from a certain degree of blurring image details rather than a true noise removal capability and does not represent an overall improvement in image quality.