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

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.