Light field angular super-resolution (LFASR) aims to reconstruct densely sampled angular views from sparsely captured inputs, enabling high-fidelity rendering, refocusing, and depth estimation. In this paper, we propose a novel LFASR framework that employs a tri-visualization feature extraction strategy, which jointly processes Sub-Aperture Images (SAIs), Epipolar Plane Images (EPIs), and Macro-Pixel Images (MacroPIs) to comprehensively exploit the spatial-angular structure of light fields. These complementary representations are processed in parallel to extract diverse and informative features, which are then refined through a deep spatial aggregation module composed of residual blocks. The proposed pipeline consists of three key stages: Early Feature Extraction (EFE), Advanced Feature Refinement (AFR), and Angular Super-Resolution (ASR). Extensive experiments on both synthetic and real-world …