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

2026-07-01

AI Summary

Existing light field super-resolution methods struggle to effectively model spatial-angular correlations while preserving 4D ray consistency, primarily due to dimension-decoupled strategies and scanning mechanisms that violate epipolar geometry. To address these limitations, this work proposes SMART, a hybrid network that uniquely integrates angular priors into spatial modeling. SMART synergistically combines a slope-guided Mamba module with an angle-optimized Transformer, complemented by an angle-modulated spatial module and a manifold alignment trajectory module that adheres to epipolar geometry. This architecture enables geometrically consistent cross-dimensional feature learning. Evaluated on five benchmarks, the proposed method achieves state-of-the-art performance, yielding an average PSNR gain of 0.42 dB and significantly suppressing visual artifacts.

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.

Problem

• Light Field Super-Resolution
• Spatial-Angular Correlation
• Epipolar Geometry
• 4D Ray Coherence
• Sequence Modeling

Innovation

• Slope-Guided Mamba
• Angular-Refined Transformer
• light field super-resolution
• epipolar geometry
• spatial-angular correlation