$L^2$FMamba: Lightweight Light Field Image Super-Resolution with State Space Model

• URL: http://arxiv.org/abs/2503.19253v1
• Date: Tue, 25 Mar 2025 01:24:52 GMT
• Title: $L^2$FMamba: Lightweight Light Field Image Super-Resolution with State Space Model
• Authors: Zeqiang Wei, Kai Jin, Zeyi Hou, Kuan Song, Xiuzhuang Zhou,
• Abstract summary: Transformers bring significantly improved performance to the light field image super-resolution task due to their long-range dependency modeling capability.<n>We 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.<n>We propose a lightweight network, $L2$FMamba, which integrates the LF-VSSM block to leverage light field features for super-resolution tasks while overcoming the computational challenges of Transformer-based approaches.
• Score: 3.741194134589865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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