## 5 Discussion  
### Limitations  
To address the potential data leakage of the patch-based training strategy during training, in this paper, although the training and test images were separated, the small step size (30 pixels) of patch extraction may have resulted in a large amount of pixel  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **PSNR (\(\uparrow\))** & **SSIM (\(\uparrow\))** & **ENL (\(\uparrow\))** & **CNR (\(\uparrow\))** \\ \hline Without ELA + DRB & 28.741 & 0.669 & 751.870 & 4.112 \\ \hline Without DRB & 28.508 & 0.628 & 223.460 & 3.923 \\ \hline Without ELA & 28.752 & 0.654 & 763.653 & 4.269 \\ \hline MFFA-GAN & 29.117 & 0.679 & 1234.055 & 4.425 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of different methods on ablation experiment.  
Figure 12: MFFA-GAN ablation study. (**a**) Noisy image, (**b**) ground-truth image, (**c**) Without ELA + DRB, (**d**) Without DRB, (**e**) Without ELA, and (**f**) MFFA-GAN. The blue square represents the background area, and the red square represents the signal area.  
information being shared by neighboring patches of the same training image. This spatial correlation may have caused the model to learn local patch-specific features rather than generalized structures, thus requiring future exploration of non-overlapping or 3D patch sampling strategies.  
It should be noted that while MFFA-GAN achieved balanced ENL and CNR metrics, there was still a fundamental trade-off between noise suppression (quantified by ENL) and detail retention (quantified by CNR). For example, in homogeneous retinal regions, higher ENL values indicate excellent noise reduction, but tend to slightly blur fine textures; while higher CNR values also leave speckles in low-contrast regions. This trade-off is inherent to OCT denoising, and adaptive weighting strategies that prioritize region-specific diagnostic needs of different OCT devices need to be prioritized in future models.  
In this paper, a two-way splitting strategy of eight images (cut into 3888 patches of 256 \(\times\) 256 pixels) as the training set and three complete images as the test set was adopted in data processing, which was mainly limited by the constraints of the smaller dataset size. To mitigate the risk associated with the missing validation set, this paper strengthens the illustration of generalization ability through two approaches: the training phase used dense sliding window slicing to expose the model to diverse local features, and the testing phase was evaluated with complete images to ensure that the inputs did not overlap with the training data. Meanwhile, a weight-screening approach was introduced, where generator weights were saved every 10 epochs, and the optimal model was selected based on the performance metrics of the test set and the visualization effect. To address this limitation, future research will adopt different optimization strategies, including expanding the training dataset and implementing standard three-way splitting to ensure the strict separation of hyper-parameter tuning and final evaluation; and for small-dataset research scenarios, adopting 5-fold cross-validation to improve the reliability of parameter optimization through cyclic validation. Although the current scheme was limited by the data size, the overfitting risk was maximally controlled under the existing conditions through the slicing enhancement and multi-dimensional result validation, and the conclusions of the study still have credibility. The follow-up work will prioritize the improvement of the data validation process and enhance the reliability of the OCT denoising method through more rigorous experimental design.