## 2 Related Work
### Image Inpainting  
Single-image inpainting aims to reconstruct the missing or masked regions in a single image with realistic content. Traditional techniques, such as anisotropic diffusion [45] and partial differential equations [46], have been effective for small occlusions but often produce over-blurred results when dealing with larger occlusions. Patch-based methods [47; 60] have been developed to address this limitation by identifying and copying similar textures from other parts of the image. However, these methods can struggle with generating \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Key Features** & **Limitations** \\ \hline \multicolumn{3}{c}{**Image Inpainting**} \\ \hline Anisotropic Diffusion [45] & Effective for small occlusions & Produces over-blurred results for larger areas, sensitive to noise, and requires careful tuning of parameters. \\ PDEs [46] & Fills missing areas using PDEs & Struggles with large occlusions, less effective for complex textures, and computationally intensive for high resolutions. \\ PatchMatch [47] & Identifies and copies similar textures & Fails to create semantically coherent structures, limited by the availability of suitable patches, and can produce visible beams. \\ Partial Convolution [11] & Encodes contextual features & Limited by invalid patterns, may require careful initialization, and struggles with large occlusions. \\ RFR [48] & Hierarchical vector quantized VAE & Blurry results for large continuous holes, sensitive to the choice of hyperparameters, and can be slow in inference. \\ LBAM [49] & Attention mechanisms in encoder–decoder & Requires large datasets for training, potentially slow convergence, and struggles with complex occlusions due to semantic limitations. \\ Single Image Inpainting [50] & Propagates information to light-field views & Limited to central view inpainting, struggles with depth variability, and may not adequately capture occlusions from different angles. \\ \hline \multicolumn{3}{c}{**Light-Field Occlusion Removal**} \\ \hline Synthetic Aperture [51] & Resampling captured light & Struggles with large occlusions, limited in handling dynamic scenes, and requires extensive pre-processing. \\ Energy Minimization [52] & Distinguishes occlusion from background & Limited to specific depth ranges, sensitive to noise, and computationally expensive. \\ Layered Imaging [4] & Depth-independent all-in-focus imaging & Limited to specific scene types, requires accurate depth information, and struggles with occlusions in dense environments. \\ K-means Clustering [53] & Iterative reconstruction framework & Ineffective for complex structures, sensitive to initialization, and struggles with real-time applications. \\ DeOccNet [54] & End-to-end network with ASPP & Results often blurry, struggles with large occlusions, and requires substantial training data. \\ MasktD [55] & 4D convolution for spatial layout & Requires extensive computational resources, complex implementation, and potentially slow inference times. \\ GANs [56] & Synthesizes results with reconstructed backgrounds & Struggles with very large occlusions, sensitive to training instability, and requires large datasets. \\ Shifted Lenslet Filtering [57] & Extracts features from lenslet images & High memory and preprocessing requirements, struggles with dense light fields, and may fail in complex scenes. \\ ISTY Framework [58] & Modularizes feature extraction & Challenges with varying disparity ranges, requires careful parameter tuning, and still struggles with large occlusions. \\ Hybrid CNN-Transformer [59] & Combines CNNs and Transformers & Still performs poorly in big occlusions, complexity in training, and potential inefficiencies in real-time applications. \\ Ours & CSPDarknet53 with BiFFN + HINet Layer & Challenges with very complex occlusions, sensitivity to hyperparameters, and requires extensive fine-tuning. \\ \hline \hline \end{tabular}
\end{table}