## 4 Results
### Experiment with Data-2  
To further evaluate the generalizability of our model, we performed the same series of experiments on Data-2. Figure 9 shows the experimental results for the comparison of the visual effects after denoising, which are basically in line with the previous performance on Data-1. It is obvious from the images that NLM (Figure 9c) and BM3D (Figure 9d) still performed poorly in denoising, and the deep learning-based denoising methods provided good visual improvement but still had some problems in retaining the structural information and removing the scatter noise well. MFFA-GAN (Figure 9l) performed well in reducing the scatter noise, and at the same time, it succeeded in retaining the rich and fine features of the retinal structure. The laminar structure of the retina was clearly visible throughout the ROI examination. \begin{table}
\begin{tabular}{c c c c c} \hline  & **PSNR (\(\uparrow\))** & **SSIM (\(\uparrow\))** & **ENL (\(\uparrow\))** & **CNR (\(\uparrow\))** \\ \hline NLM & 25.452 & 0.428 & 27.300 & 1.929 \\ BM3D & 27.309 & 0.559 & 51.893 & 2.246 \\ DnCNN & 27.618 & 0.469 & 49.743 & 2.258 \\ SDSR-OCT & 29.859 & 0.642 & 137.385 & 2.638 \\ DudeNet & 29.647 & 0.706 & 285.529 & 2.885 \\ MDGAN & 28.912 & 0.713 & 594.706 & 3.286 \\ MDRGAN & 28.917 & 0.689 & 286.261 & 3.102 \\ DCANet & 29.208 & 0.726 & 907.062 & 3.375 \\ DRANet & 29.693 & 0.675 & 198.857 & 3.165 \\ MFFA-GAN & 30.107 & 0.727 & 529.161 & 3.927 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of different methods on Data-1. The performance of MFFA-GAN in Data-2, which included normal eyes and early non-neovascular age-related macular degeneration (AMD) retinas, is demonstrated by the quantitative evaluation results of ten different denoising techniques on 24 OCT images, as shown in Table 2. Compared with the traditional BM3D method, MFFA-GAN achieved a significant improvement of about 1.6 dB in PSNR and also performed well in the SSIM metric, which is a measure of image structural similarity. Specifically, the model achieved 4.137 and 629.303 in CNR (contrast-to-noise ratio) and ENL (equivalent number of visual lines), respectively, which are the key diagnostic indicators of subtle vitreous warts and retinal pigment epithelium (RPE) irregularities in the early stage of AMD. A high CNR effectively enhances the visibility of hypo-reflective vitreous warts in the context of the RPE, and the feature of CNR in the NLM (CNR) is also well recognized in the NLM (NLM). This feature is often masked by noise interference in conventional methods, such as NLM (CNR = 2.413) and BM3D (CNR = 2.926). In addition, the model avoided excessive smoothing of the retinal layers by balancing the ENL values, allowing for the retention of key microstructural details, which are important diagnostic features in the pre-AMD stage. It can be seen that MFFA-GAN effectively removed the speckle noise from OCT images while retaining the edge details and overall structural information of the images. The overall performance of MFFA-GAN was better than that of other schemes in the processing of OCT images in Data-2. Figure 8: Pixel error map between denoised image and real image: (**a**) reference, (**b**) NLM, (**c**) BM3D, (**d**) DnCNN, (**e**) SDSR-OCT, (**f**) DudeNet, (**g**) MDGAN, (**h**) MDRGAN, (**i**) DCANet, (**j**) DRANet, and (**k**) MFFA-GAN. As shown in Figure 10, in the error heatmap of Data-2, the MFFA-GAN (Figure 10k) presents more white color in core intermediate diagnostic regions, such as the retinal layer, which corresponds to the quantitative dominance of CNR and ENL. However, there were some errors at the boundary of k, reflecting the results of the model's trade-off between global noise suppression and local structural fidelity. Overall, MFFA-GAN effectively improved the diagnostic accuracy of retinopathy by focusing on the high-fidelity denoising of the core diagnostic region, enhancing the interpretability of the results and providing a direction for subsequent optimization.