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Light field (LF) camera captures rich information from a scene. Using theinformation, the LF de-occlusion (LF-DeOcc) task aims to reconstruct theocclusion-free center view image. Existing LF-DeOcc studies mainly focus on thesparsely sampled (sparse) LF images where most of the occluded regions arevisible in other views due to the large disparity. In this paper, we expandLF-DeOcc in more challenging datasets, densely sampled (dense) LF images, whichare taken by a micro-lens-based portable LF camera. Due to the small disparityranges of dense LF images, most of the background regions are invisible in anyview. To apply LF-DeOcc in both LF datasets, we propose a framework, ISTY,which is defined and divided into three roles: (1) extract LF features, (2)define the occlusion, and (3) inpaint occluded regions. By dividing theframework into three specialized components according to the roles, thedevelopment and analysis can be easier. Furthermore, an explainableintermediate representation, an occlusion mask, can be obtained in the proposedframework. The occlusion mask is useful for comprehensive analysis of the modeland other applications by manipulating the mask. In experiments, qualitativeand quantitative results show that the proposed framework outperformsstate-of-the-art LF-DeOcc methods in both sparse and dense LF datasets.

I See-Through You: A Framework for Removing Foreground Occlusion in Both Sparse and Dense Light Field Images