## 2 Related Work
### Light-Field Occlusion Removal  
LF imaging has gained traction due to the development of portable plenoptic cameras, which capture both the spatial and angular information of light rays, enabling a richer representation of scenes. This 4D light radiation field allows for the inference of depth information and reconstructing occluded objects more accurately than single-view methods. Early research leveraged the synthetic aperture focusing method, as proposed by [51], which involved resampling the captured light to blur the foreground and focus on the background. Despite enhancements to this approach, including alternative cost functions and dense multi-camera setups for recording light-field video, these methods struggled with large occlusions and lacked the ability to distinguish between foreground occlusion pixels and background occluded pixels. Ref. [52] addressed this limitation by labeling each pixel in every view through energy minimization to distinguish between occlusion and background. They later improved this technique by introducing a depth-free, all-in-focus synthetic aperture imaging method based on LF visibility analysis. However, these methods were limited to specific depth ranges, leaving objects at other depths blurred. [PERSON]. [4] addressed this limitation by dividing scenes into visible layers, enabling depth-independent all-in-focus imaging. [PERSON]. [53] developed an iterative reconstruction approach within a global optimization framework by using k-means clustering to classify occlusion and background pixels, refining results using a coarse-to-fine strategy. Despite these advancements, traditional methods based on handcrafted features and stereo matching techniques struggled in scenes with complex structures and heavy occlusions, leading to the exploration of learning-based approaches. The advent of learning-based methods has significantly advanced LF occlusion removal. DeOccNet, proposed by [54], was the first end-to-end network for LF occlusion removal, utilizing a deep encoder-decoder model with residual atrous spatial pyramid pooling. This model also introduced a mask embedding approach to generate training datasets, allowing for effective supervised learning. However, DeOccNet's results were often blurry and struggled with large occlusions. Subsequent improvements came with Mask4D, presented by [55], which maintained the spatial layout of SAIs and employed 4D convolution to fully extract angular information, thereby improving de-occlusion performance. [PERSON]. [56] leveraged Generative Adversarial Networks to semantically inpaint occluded regions, synthesizing the results with reconstructed backgrounds to produce occlusion-free images. [PERSON]. [57] introduced a filter for extracting features from shifted lenslet images to reconstruct occluded regions. While effective for sparse LFs, this method faced challenges with dense LFs due to assumptions about background visibility and the high memory and preprocessing requirements. To address both sparse and dense LF images, [58] introduced the ISTY framework for light-field de-occlusion, which modularizes the process into three distinct roles: extracting light-field features, defining occlusions, and inpainting occluded regions. The framework effectively manages the challenges posed by varying disparity ranges and enhances performance in both sparse and dense LF datasets. While these CNN-based methods showed promise, they were limited by the inherent local receptive field of CNNs, leading to incomplete occlusion removal and quality decline in complex scenes. To mitigate this limitation, a sophisticated approach proposed by [59] that synergistically combines Convolutional Neural Networks (CNNs) and Swin Transformers was developed to advance occlusion removal; CNNs were employed in the shallow layers to capture intricate local features and details, while Swin Transformers were used in the deeper layers to model the global patterns of large occlusions.