$L^2$FMamba: Lightweight Light Field Image Super-Resolution with State Space Model
Journal:
arXiv
Published Date:
Mar 25, 2025
Abstract
Transformers bring significantly improved performance to the light field
image super-resolution task due to their long-range dependency modeling
capability. However, the inherently high computational complexity of their core
self-attention mechanism has increasingly hindered their advancement in this
task. To address this issue, we first introduce the LF-VSSM block, a novel
module inspired by progressive feature extraction, to efficiently capture
critical long-range spatial-angular dependencies in light field images. LF-VSSM
successively extracts spatial features within sub-aperture images,
spatial-angular features between sub-aperture images, and spatial-angular
features between light field image pixels. On this basis, we propose a
lightweight network, $L^2$FMamba (Lightweight Light Field Mamba), which
integrates the LF-VSSM block to leverage light field features for
super-resolution tasks while overcoming the computational challenges of
Transformer-based approaches. Extensive experiments on multiple light field
datasets demonstrate that our method reduces the number of parameters and
complexity while achieving superior super-resolution performance with faster
inference speed.
