## 6 Conclusions  
For efficient occlusion removal in light-field images, we have presented a comprehensive architecture in this work that combines CSPDarknet53 and the BiFPN. Using sophisticated feature extraction and multi-scale fusion techniques, this architecture tackles the significant difficulty of handling large and complicated occlusions. Robust feature extraction is provided by CSPDarknet53, while feature integration is improved by BiFPN. Efficiency is guaranteed without sacrificing feature extraction quality because of the incorporation of separable convolutional blocks. Furthermore, thorough image refining is made possible by the HINet, which efficiently addresses both local and global details. The network can thoroughly handle occlusions of different sizes and complexity thanks to this multi-perspective method. The originality of the proposed method lies in its efficient multi-scale feature extraction through CSPDarknet53, which reduces computational overhead while effectively managing occlusions. The dynamic feature fusion introduced by BiFPN ensures that both fine and coarse details are preserved, even in large occlusions. Additionally, the integration of HINet provides meticulous refinement by addressing local occlusion details and global structures, leading to smoother and more accurate reconstructions. The use of separable convolutions also enhances computational efficiency without sacrificing performance, making the method scalable for larger datasets and applicable in real-world scenarios. Numerous studies on diverse datasets with different degrees of occlusion severity show notable gains over cutting-edge techniques. Overall, the proposed architecture represents a significant advancement in occlusion removal, enhancing the accuracy of light-field imaging. By effectively addressing complex occlusions, it offers promising applications across various fields and sets the stage for further development in real-world scenarios.  
**Author Contributions:** Conceptualization, M.F.S.; Methodology, M.F.S.; Software, M.F.S.; Validation, M.F.S. and H.-S.K.; Formal analysis, M.F.S.; Investigation, H.-S.K.; Resources, H.-S.K.; Data curation, M.F.S.; Writing--original draft, M.F.S.; Writing--review and editing, M.F.S. and H.-S.K.; Visualization, M.F.S. and H.-S.K.; Supervision, H.-S.K.; Project administration, H.-S.K.; Funding acquisition, H.-S.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The Basic Science Research Program partially supported this research through the National Research Foundation of Korea (NRF), funded by the Ministry of Education under Grant 2020R1I1A3A04037680, and partly by Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (IITP-2024-2020-0-01462, 30%).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.