Robust light field angular super-resolution via multi-dimensional feature fusion and attention-guided refinement

Xiyao Hua, Boni Su and Daili Yang

PLOS ONE, 2026, vol. 21, issue 7, 1-26

Abstract: Angular super-resolution (ASR) is a fundamental task in light field (LF) imaging, aimed at reconstructing a dense LF from sparsely sampled views. Despite significant progress, current methods often struggle to preserve consistency in complex scenarios such as severe occlusions and large-disparity regions. In this paper, we propose a robust attention-guided multi-dimensional feature fusion network (LFAMF) for LF angular reconstruction. The proposed framework comprises two synergistic stages: a multi-dimensional feature fusion stage and an attention-guided refinement stage. Specifically, we design a multi-stream subnetwork (MFNet) to extract intrinsic physical characteristics across the spatial, angular, EPI, and pseudo-video sequence domains. Simultaneously, a geometry-prior-based subnetwork (GSPNet) is incorporated to leverage scene structure for improved texture preservation. To effectively integrate these complementary streams, an attention-guided fusion subnetwork (AFNet) is employed to adaptively merge intermediate results. Extensive experiments on both synthetic and real-world datasets demonstrate that the LFAMF model significantly outperforms state-of-the-art methods, particularly in maintaining structural integrity at occlusion boundaries and highly textured areas while ensuring superior angular consistency.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0353845

DOI: 10.1371/journal.pone.0353845

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