## 4 Experiments
### Ablation Study  
In this section, we conduct a thorough ablation study to assess the contributions of various components in our proposed architecture, as illustrated in Table 4 and Figure 8. Our ablation study aims to identify the optimal number of BiFPN layers required for model convergence and evaluate the effects of selectively omitting key modules, including the complete removal of the BiFPN layers and the exclusion of the HiNet refinement module on the network's overall performance in light-field occlusion removal. The network is retrained using the same training data.  
Our study begins by evaluating the model's performance across configurations with 3, 5, 6 (our baseline), and 7 BiFPN layers, as well as a variant that omits all BiFPN layers. The baseline model, featuring 6 BiFPN layers, consistently outperformed the others, achieving the highest performance metrics. As marked in red in the table, it attained an average PSNR of 28.104 dB and an SSIM of 0.858. This result underscores the model's effective balance between complexity and feature extraction, enabling it to capture rich, multi-scale contextual information crucial for accurately reconstructing occluded regions. Reducing the number of BiFPN layers to 3 or 5 resulted in a noticeable decline in performance metrics. This decrease can be attributed to the model's reduced capacity to capture rich contextual features, as fewer layers limited its ability to aggregate multi-scale information. The lack of sufficient multi-scale representations led to inadequate feature aggregation and impaired the model's ability to discern occluded regions effectively. Interestingly, increasing the number of BiFPN layers to 7 did not yield a proportional improvement in performance. This suggests that, while additional layers can enhance feature interactions, they may also introduce complexity and noise, resulting in diminishing returns and potential overfitting. This highlights the importance of balancing model depth with effective generalization across diverse input conditions.  
We also investigated the contribution of the HiNet refinement module by training a version of our model without it. The exclusion of the HiNet layer led to a significant de  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **LBAM [48]** & **DeOccNet [54]** & **[PERSON] et al. [57]** & **ISTY [58]** & **Ours** \\ \hline
**Params**\(\downarrow\) & 69.3 M & 39.0 M & 2.7 M & 80.6 M & 52.59 M \\
**Inf**\(\downarrow\) & 12 ms & 10 ms & 3050 ms & 24 ms & 138.8 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of each model’s parameters and average inference time (Inf) for processing 256 \(\times\) 192 light-field images on an Nvidia Geforce RTX 4090 GPU. \(\downarrow\) indicates that smaller results are better.  
crease in both PSNR and SSIM, emphasizing its critical role in enhancing detail and texture in the output. The HiNet layer enables selective refinement of feature maps produced by the preceding BiFPN layers, particularly enhancing intricate textures and edges, which significantly improves the clarity of reconstructed occluded regions. In summary, our ablation study demonstrates that the baseline model with 6 BiFPN layers is pivotal for achieving optimal performance in occlusion removal. Both the appropriate number of BiFPN layers and the inclusion of the HiNet refinement module are crucial for the model's effectiveness in reconstructing occluded regions, validating our architectural design choices.