Occlusion Removal in Light-Field Images Using CSPDarknet53 and Bidirectional Feature Pyramid Network: A Multi-Scale Fusion-Based Approach  
[PERSON]  
1 School of Information and Communication Engineering, Chungbuk National University,  
Cheongju 28644, Republic of Korea; [EMAIL_ADDRESS]  
2 Information Technology Department, Faculty of Computers and Information, Assiut University,  
Assiut 71526, Egypt  
2  
[PERSON]  
1 School of Information and Communication Engineering, Chungbuk National University,  
Cheongju 28644, Republic of Korea; [EMAIL_ADDRESS]  
2 Information Technology Department, Faculty of Computers and Information, Assiut University,  
Assiut 71526, Egypt  
2  
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encountered in conventional single-view imaging. This enables algorithms to reconstruct occluded areas by leveraging information from unoccluded views, where obscured pixels in one view remain visible in others. But the challenge becomes significantly pronounced when dealing with large and complex occlusions that obscure critical details. Traditional methods, often struggle with these scenarios due to their limited ability to capture extensive contextual information and integrate multi-scale features effectively. Recent advancements have sought to address these limitations by incorporating more sophisticated network architectures and multi-scale feature integration techniques. However, many of these methods still fall short when faced with large occlusions, primarily due to their inadequate handling of global and local feature dependencies. To overcome these challenges, a more holistic approach that combines robust feature extraction, efficient multi-scale fusion, and meticulous image refinement is essential. In response to these issues, we propose a novel architecture that synergistically combines CSPDarknet53 [42] and the bidirectional feature pyramid network (BiFPN) [43] for efficient light-field occlusion removal. CSPDarknet53, known for its robust and rich feature extraction capabilities across multiple scales, serves as the backbone of our network. It effectively captures detailed local features while maintaining a high degree of computational efficiency. To enhance the integration of these features, we incorporate BiFPN, which facilitates advanced multi-scale fusion and attention mechanisms. BiFPN's ability to aggregate and refine features across different scales ensures comprehensive integration, thereby improving the network's capability to handle occlusions of varying sizes and complexities. Additionally, we employ separable convolutional blocks within BiFPN to maintain computational efficiency without compromising the quality of feature extraction. To further refine the reconstructed images and address both local details and global structures, we integrate the Half-Instance Initialization Network (HINet) [44]. HINet meticulously refines the images, ensuring that even the smallest details are preserved while maintaining the integrity of the overall structure. This multi-faceted approach allows our network to effectively handle occlusions, providing a robust solution for comprehensive occlusion removal. We conducted many experiments on multiple sparse and dense datasets with different degrees of occlusion severity to verify the efficiency of the proposed approach.