## 5 Limitations and Future Directions  
While the proposed architecture represents a significant advancement in occlusion removal for light-field images, several limitations must be addressed to further enhance its robustness and practical applicability, particularly in handling large foreground occlusions that obscure significant scene areas, leading to background information loss. Although the multi-scale feature fusion strategy offers some mitigation, background recovery in heavily \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**LF Type** & \multicolumn{2}{c}{**Sparse (Syn)**} & \multicolumn{2}{c}{**Sparse (Real)**} & \multicolumn{2}{c}{**Dense (Syn)**} \\ \hline
**Name** & **4-Syn** & **9-Syn** & **CD** & **Single Occ** & **Double Occ** \\ \hline  & \multicolumn{4}{c}{**PSNR \(\uparrow\)**} \\ \hline Ours w/o BiFPN & 20.04 & 20.56 & 20.28 & 28.31 & 27.62 \\ Ours w/ 3 BiFPN & 26.73 & 26.99 & 24.90 & 26.36 & 27.06 \\ Days w/ 5 BiFPN & 26.47 & 27.27 & 25.17 & 30.59 & 29.25 \\ Days & 24.59 & 25.42 & 23.71 & 29.05 & 27.56 \\ Ours w/o & 26.53 & 27.16 & 24.85 & 27.84 & 26.51 \\ Refinement & 27.32 & 27.48 & 25.68 & 30.70 & 29.34 \\ \hline  & \multicolumn{4}{c}{**SSIM \(\uparrow\)**} \\ \hline Ours w/o BiFPN & 0.547 & 0.534 & 0.701 & 0.789 & 0.812 \\ Ours w/ 3 BiFPN & 0.828 & 0.841 & 0.833 & 0.795 & 0.822 \\ Days & 0.832 & 0.849 & 0.863 & 0.835 & 0.847 \\ Ours w/ 7 BiFPN & 0.748 & 0.784 & 0.805 & 0.830 & 0.835 \\ Days & 0.829 & 0.838 & 0.849 & 0.824 & 0.845 \\ Refinement & \multicolumn{4}{c}{**OSN**} \\ \hline Ours & 0.862 & 0.853 & 0.886 & 0.838 & 0.850 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Conducting ablation studies on our method and its variants across sparse and dense light-field datasets. Performance is evaluated using PSNR and SSIM (PSNR/SSIM), with top results marked in red. \(\uparrow\) indicates that a higher result is better. Figure 8: A visual exploration of model elements: unveiling the role of each component in our ablation study. occluded regions remains inadequate. Future work should enhance global context awareness and advanced depth estimation techniques, as well as explore more extensive camera array configurations to improve recovery in complex scenes. One limitation is the high computational complexity associated with the architecture, which can hinder deployment in resource-constrained environments. Future work should focus on reducing the number of network parameters without compromising performance. Techniques such as network pruning, quantization, and knowledge distillation could be employed to compress the model. Additionally, exploring lightweight backbone networks architectures could yield a more efficient configuration. Another challenge is the limited generalization of the model across complex occlusion scenarios. The model may struggle with unseen occlusion patterns that were not well represented in the training dataset. To address this, expanding the training datasets to include a wider range of real-world occlusions is essential. Incorporating self-supervised or unsupervised learning approaches could further enhance the model's ability to learn from unlabelled data. Moreover, the model may struggle to handle large disparities effectively. In cases where occlusions span multiple depth planes, the current architecture may not adequately capture the necessary context. Introducing 3D or volumetric approaches that account for spatial relationships within the light-field data could significantly enhance the model's ability to manage large disparities. Finally, while the model excels at removing occlusions, it does not currently incorporate inpainting techniques to fill in areas left by these removals. This can lead to incomplete reconstructions in certain cases. Future work should explore integrating inpainting mechanisms or generative adversarial networks to generate plausible content in occluded regions, thereby enhancing the completeness and quality of the output.