Slope-Guided Mamba and Angular-Refined Transformer for Light Field Super-Resolution

• URL: http://arxiv.org/abs/2607.00965v1
• Date: Wed, 01 Jul 2026 14:01:21 GMT
• Title: Slope-Guided Mamba and Angular-Refined Transformer for Light Field Super-Resolution
• Abstract summary: Light Field Super-Resolution (LFSR) necessitates accurate modeling of spatial-angular correlations.<n>Maintaining such high-dimensional consistency is challenging due to two inherent limitations in prevailing modeling paradigms.<n>We propose a hybrid light field super-resolution network, termed SMART, which integrates a Slope-Guided Mamba and an Angular-Refined Transformer.<n> Experiments on five benchmarks demonstrate that SMART achieves state-of-the-art performance, surpassing previous methods by 0.42 dB (PSNR) with significantly reduced artifacts.
• Score: 13.838595570912227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Light Field Super-Resolution (LFSR) necessitates accurate modeling of spatial-angular correlations while preserving intrinsic 4D ray coherence. However, maintaining such high-dimensional consistency remains challenging, primarily due to two inherent limitations in prevailing modeling paradigms. First, spatial and angular dimensions are often modeled in a decoupled manner, restricting early cross-dimensional interaction and leading to geometric inconsistencies. Moreover, although continuous sequence modeling paradigms show promise in representing epipolar structures, their rigid scanning mechanisms fundamentally conflict with epipolar geometry, limiting geometry-aware feature aggregation. To address these challenges, we propose a hybrid light field super-resolution network, termed SMART, which integrates a Slope-Guided Mamba and an Angular-Refined Transformer to effectively overcome these limitations. Specifically, we introduce an angular-modulated spatial module to bridge the decoupling gap, incorporating angular priors to strengthen spatial-angular correlation modeling. To mitigate the scan-geometry mismatch, we propose a manifold-aligned trajectory module that enables geometry-consistent sequence modeling along epipolar structures. Experiments on five benchmarks demonstrate that SMART achieves state-of-the-art performance, surpassing previous methods by 0.42 dB (PSNR) with significantly reduced artifacts.

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