Mask-Aware Light Field De-Occlusion With Gated Feature Aggregation and Texture-Semantic Attention

Abstract

A light field image records rich information of a scene from multiple views, thereby providing complementary information for occlusion removal. However, current occlusion removal methods have several issues: 1) inefficient exploitation of spatial and angular complementary information among views; 2) indistinguishable treatment of pixels from foreground occlusion and background; and 3) insufficient exploration of spatial detail supplementation. Therefore, in this article, we propose a mask-aware de-occlusion network (MANet). Specifically, MANet is a joint training network that integrates the occlusion mask predictor (OMP) and the occlusion remover (OR). First, OMP is proposed to provide the location of occluded regions for OR, as the occlusion removal task is ill-posed without occluded region localization. In OR, we introduce gated spatial-angular feature aggregation, which uses a soft gating mechanism to focus on spatial-angular interaction features in non-occluded regions, extracting effective aggregated features specific to the de-occlusion. Then, we design a complementary strategy to fully utilize spatial-angular information among views. Finally, we propose texture-semantic attention to improve the performance of detail generation. Experimental results demonstrate the superiority of MANet, with substantial improvements in both PSNR and SSIM metrics. Moreover, MANet stands out with an efficient parameter count of 2.4 M, making it a promising solution for real-world applications in public safety and security surveillance.