## 4 Experiments
### Experimental Results  
#### 4.2.1 Quantitative Results  
We assess how accurate the de-occluded images are quantitatively by comparing our model to cutting-edge LF occlusion removal techniques: DeOccNet [54], as well as the approaches given by [PERSON] et al. [57] and ISTY [58]. We also analyze the information obtained from different views in light fields (LFs) by contrasting our model with single-image inpainting techniques, namely, RFR [48] and LBAM [49]. We used the same learning approach and mask embedding for a fair comparison inside the dense LF dataset and trained DeOccNet from scratch on our dataset. We applied the ISTY [58] approach using the provided authors' weights. Because of the restricted nature of the training data for RFR [48] and LBAM [49], and because [PERSON] et al. [57] do not provide access to their source codes, the quantitative findings for all three approaches are derived directly from ISTY [58]. The quantitative findings are presented in Table 2. For assessing the image quality, we utilized PSNR and SSIM, two well-known metrics used in LF occlusion-removal studies.  
While DeOccNet [54] shows limited performance overall, it achieves decent results across both datasets. With the exception of single-disparity occlusions in sparse LFs, [PERSON] et al. [57] do not consistently produce improved results. Although ISTY [58] shows a  
\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{6}{c}{**PSNR \(\uparrow\)**} \\ \hline RFR [48] & 19.89 & 20.69 & 21.13 & 26.28 & 23.25 \\ LBAM [49] & 21.11 & 23.04 & 21.56 & 27.92 & 24.83 \\ DeOccNet [54] & 23.74 & 23.70 & 22.70 & 28.67 & 25.85 \\ [PERSON] et al. [57] & 14.46 & 22.00 & 20.19 & 23.15 & 18.01 \\ ISTY [58] & 26.42 & 27.04 & 25.17 & 32.44 & 28.31 \\ Ours & 27.32 & 27.48 & 25.68 & 30.70 & 29.34 \\ \hline \multicolumn{6}{c}{**SSIM \(\uparrow\)**} \\ \hline RFR [48] & 0.668 & 0.672 & 0.646 & 0.867 & 0.801 \\ LBAM [49] & 0.677 & 0.725 & 0.803 & 0.899 & 0.827 \\ DeOccNet [54] & 0.701 & 0.715 & 0.741 & 0.914 & 0.847 \\ [PERSON] et al. [57] & 0.683 & 0.758 & 0.832 & 0.900 & 0.823 \\ ISTY [58] & 0.836 & 0.849 & 0.870 & 0.947 & 0.902 \\ Ours & 0.862 & 0.853 & 0.886 & 0.838 & 0.850 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparison on the sparse and dense LF dataset using PSNR and SSIM. \(\uparrow\) indicates that a higher result is better; red indicates the best result, while blue indicates the second-best result.  
reasonable inference time and practical applicability, the pre-trained inpainting models suffer from catastrophic forgetting, and the inpainting knowledge used is currently suboptimal due to limited training data. Furthermore, it requires more parameters for dense light fields (LFs) than for sparse ones, making it twice as large as DeOccNet [54]. The single-image inpainting techniques RFR [48] and LBAM [49] perform better on dense LF images because they are adept at handling occlusion-free scenes. But, since these models cannot use the background information from LFs, they perform worse on sparse LFs. As quantitative measures show, our proposed solution outperforms existing LF occlusion removal and inpainting models in sparse light fields (LFs) and performs competitively in dense LFs.