提出新模型提升光场图像超分辨率，更好融合多视角信息。
RASLF: Representation-Aware State Space Model for Light Field Super-Resolution
- 设计多表示感知框架，显式建模不同光场表示间的结构关联。
- 在多个公开数据集上实现最高重建精度，计算效率高。
- 适合关注光场重建与高效视觉模型的科研人员。
当前基于状态空间模型的光场超分辨率方法常未能充分利用多种光场表示间的互补性，导致细节纹理丢失和视图间几何错位。为此，本文提出RASLF，一种表示感知的状态空间框架，显式建模多光场表示间的结构相关性。具体地，设计了渐进式几何精修（PGR）模块，利用全景对极表示显式编码多视角视差差异，实现不同光场表示间的有效融合；引入表示感知非对称扫描（RAAS）机制，根据不同表示空间的物理特性动态调整扫描路径，通过路径剪枝优化性能与效率的平衡；此外，双锚点聚合（DAA）模块改善层次化特征流动，减少深层冗余特征，优先保留关键重建信息。在多个公开基准上的实验表明，RASLF在保持高度计算效率的同时，实现了最高的重建精度。
原文摘要 · Abstract (English)
Current SSM-based light field super-resolution (LFSR) methods often fail to fully leverage the complementarity among various LF representations, leading to the loss of fine textures and geometric misalignments across views. To address these issues, we propose RASLF, a representation-aware state-space framework that explicitly models structural correlations across multiple LF representations. Specifically, a Progressive Geometric Refinement (PGR) block is created that uses a panoramic epipolar representation to explicitly encode multi-view parallax differences, thereby enabling integration across different LF representations. Furthermore, we introduce a Representation Aware Asymmetric Scanning (RAAS) mechanism that dynamically adjusts scanning paths based on the physical properties of different representation spaces, optimizing the balance between performance and efficiency through path pruning. Additionally, a Dual-Anchor Aggregation (DAA) module improves hierarchical feature flow, reducing redundant deeplayer features and prioritizing important reconstruction information. Experiments on various public benchmarks show that RASLF achieves the highest reconstruction accuracy while remaining highly computationally efficient.
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