LFTransMamba: A Hybrid Mamba-Transformer Model for Light Field Image Super-Resolution

Abstract

Light Field Super-Resolution (LFSR) seeks to enhance the spatial resolution of light field images while preserving angular consistency. Existing convolution-based networks struggle to capture long-range spatial-angular dependencies, and although Transformer-based methods address this limitation, they incur prohibitive quadratic complexity when processing high-resolution 4D light field data. In contrast, Mamba-based architectures can efficiently capture long-range dependencies but have limited capacity for contextual modeling. To overcome these challenges, we introduce LFTransMamba, a hybrid architecture that integrates Transformer-based global context modeling with Mamba-based efficient long-range dependency capture. Specifically, we propose a Masked Light Field Image Modeling (MLFIM) training strategy, which enhances the modeling of spatial-angular relationships through masked reconstruction without introducing additional modules or loss functions. Furthermore, we present an enhanced Position-Sensitive Windowing mechanism (EPSW), employing Gaussian-weighted aggregation to emphasize central pixels, thereby improving reconstruction quality and mitigating structural artifacts. Our method achieves state-of-the-art performance in the NTIRE 2025 Light Field Image Super-Resolution Challenge, ranking 1st in both the Classic and Large-Model tracks, and 2nd in the Efficiency track. The code is available at https://github.com/OpenMeow/LFTransMamba.