Transformer-based methods have demonstrated impressive performance in 4D
light field (LF) super-resolution by effectively modeling long-range
spatial-angular correlations, but their quadratic complexity hinders the
efficient processing of high resolution 4D inputs, resulting in slow inference
speed and high memory cost. As a compromise, most prior work adopts a
patch-based strategy, which fails to leverage the full information from the
entire input LFs. The recently proposed selective state-space model, Mamba, has
gained popularity for its efficient long-range sequence modeling. In this
paper, we propose a Mamba-based Light Field Super-Resolution method, named
MLFSR, by designing an efficient subspace scanning strategy. Specifically, we
tokenize 4D LFs into subspace sequences and conduct bi-directional scanning on
each subspace. Based on our scanning strategy, we then design the Mamba-based
Global Interaction (MGI) module to capture global information and the local
Spatial- Angular Modulator (SAM) to complement local details. Additionally, we
introduce a Transformer-to-Mamba (T2M) loss to further enhance overall
performance. Extensive experiments on public benchmarks demonstrate that MLFSR
surpasses CNN-based models and rivals Transformer-based methods in performance
while maintaining higher efficiency. With quicker inference speed and reduced
memory demand, MLFSR facilitates full-image processing of high-resolution 4D
LFs with enhanced performance.

本文基于选择性状态空间模型 Mamba，提出了一种基于 Mamba 的光场超分辨率方法 MLFSR，通过设计高效的子空间扫描策略来实现对高分辨率 4D 光场的全图像处理，显著提升了性能。

基于曼巴的高效子空间扫描的光场超分辨率

Mamba-based Light Field Super-Resolution with Efficient Subspace  Scanning

Depth (disparity) estimation from 4D Light Field (LF) images has been a
research topic for the last couple of years. Most studies have focused on depth
estimation from static 4D LF images while not considering temporal information,
i.e., LF videos. This paper proposes an end-to-end neural network architecture
for depth estimation from 4D LF videos. This study also constructs a
medium-scale synthetic 4D LF video dataset that can be used for training deep
learning-based methods. Experimental results using synthetic and real-world 4D
LF videos show that temporal information contributes to the improvement of
depth estimation accuracy in noisy regions. Dataset and code is available at:
this https URL

本研究提出了一种基于神经网络的端到端架构，用于从 4D 光场视频中进行深度估计，同时建立了一个中等规模的 4D 光场视频数据集，并验证了在嘈杂区域中加入时间信息可以提高深度估计精度的实验结果。