  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.
