Recent years have witnessed significant advancements in light field image
super-resolution (LFSR) owing to the progress of modern neural networks.
However, these methods often face challenges in capturing long-range
dependencies (CNN-based) or encounter quadratic computational complexities
(Transformer-based), which limit their performance. Recently, the State Space
Model (SSM) with selective scanning mechanism (S6), exemplified by Mamba, has
emerged as a superior alternative in various vision tasks compared to
traditional CNN- and Transformer-based approaches, benefiting from its
effective long-range sequence modeling capability and linear-time complexity.
Therefore, integrating S6 into LFSR becomes compelling, especially considering
the vast data volume of 4D light fields. However, the primary challenge lies in
\emph{designing an appropriate scanning method for 4D light fields that
effectively models light field features}. To tackle this, we employ SSMs on the
informative 2D slices of 4D LFs to fully explore spatial contextual
information, complementary angular information, and structure information. To
achieve this, we carefully devise a basic SSM block characterized by an
efficient SS2D mechanism that facilitates more effective and efficient feature
learning on these 2D slices. Based on the above two designs, we further
introduce an SSM-based network for LFSR termed LFMamba. Experimental results on
LF benchmarks demonstrate the superior performance of LFMamba. Furthermore,
extensive ablation studies are conducted to validate the efficacy and
generalization ability of our proposed method. We expect that our LFMamba shed
light on effective representation learning of LFs with state space models.

近年来，随着现代神经网络的进步，光场图像超分辨率（LFSR）取得了显著的进展。然而，这些方法往往在捕捉长距离依赖关系（基于 CNN）或遇到二次计算复杂性（基于 Transformer）方面面临挑战，从而限制了它们的性能。最近，基于状态空间模型（SSM）和选择性扫描机制（S6）的 Mamba 已成为各种视觉任务中传统 CNN 和 Transformer 方法的卓越替代方案，得益于其有效的长距离序列建模能力和线性时间复杂性。因此，将 S6 集成到 LFSR 中变得具有吸引力，特别是考虑到 4D 光场的大量数据量。然而，主要挑战在于设计适用于 4D 光场的适当扫描方法，以有效地对光场特征进行建模。为了解决这个问题，我们对 4D LFs 的信息性 2D 切片使用了 SSMs，以充分探索空间上下文信息、互补的角度信息和结构信息。为了实现这一点，我们精心设计了一个基本的 SSM 块，其特点是一个有效的 SS2D 机制，可以在这些 2D 切片上实现更有效和高效的特征学习。基于上述两个设计，我们进一步介绍了一种基于 SSM 的 LFSR 网络，称为 LFMamba。LF 基准测试的实验结果证明了 LFMamba 的卓越性能。此外，我们还进行了广泛的消融研究，以验证我们提出的方法的功效和泛化能力。我们希望我们的 LFMamba 能够为光场的有效表示学习提供启示。