Selected work

Representative Papers

RASLF: Representation-Aware State Space Model for Light Field Super-Resolution

Mar 17, 2026

This work addresses the limitations of existing state-space-model-based light field super-resolution methods, which often fail to effectively integrate complementary information across multiple representations, leading to loss of texture details and misalignment in view geometry. To overcome these challenges, we propose a representation-aware state space framework that explicitly models structural relationships among diverse light field representations—such as epipolar plane images and sub-aperture views—to enable accurate and efficient super-resolution reconstruction. Key innovations include a progressive geometric refinement module for view alignment correction, a representation-aware asymmetric scanning mechanism tailored to heterogeneous representation characteristics, and a dual-anchor hierarchical feature aggregation strategy. Extensive experiments demonstrate that our method achieves state-of-the-art reconstruction performance on multiple public benchmarks while maintaining superior computational efficiency.

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

Mar 25, 2025

To address the high computational complexity and low inference efficiency of Transformers in light field (LF) image super-resolution—stemming from their self-attention mechanism—this paper proposes LF-VSSM, the first lightweight state space model tailored for LF super-resolution. LF-VSSM hierarchically models long-range dependencies: intra-view spatial, inter-view spatial-angular, and pixel-level spatial-angular correlations, marking the first systematic integration of state space models into LF super-resolution. Leveraging LF geometric priors and progressive feature extraction, the network achieves significant parameter and FLOPs reduction. Extensive experiments on multiple benchmark datasets demonstrate that LF-VSSM surpasses existing state-of-the-art methods in PSNR and SSIM while reducing model size and computational cost; notably, it achieves a 42% speedup in inference time.

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Recent publications

Latest Papers

RASLF: Representation-Aware State Space Model for Light Field Super-Resolution

Mar 17, 2026

This work addresses the limitations of existing state-space-model-based light field super-resolution methods, which often fail to effectively integrate complementary information across multiple representations, leading to loss of texture details and misalignment in view geometry. To overcome these challenges, we propose a representation-aware state space framework that explicitly models structural relationships among diverse light field representations—such as epipolar plane images and sub-aperture views—to enable accurate and efficient super-resolution reconstruction. Key innovations include a progressive geometric refinement module for view alignment correction, a representation-aware asymmetric scanning mechanism tailored to heterogeneous representation characteristics, and a dual-anchor hierarchical feature aggregation strategy. Extensive experiments demonstrate that our method achieves state-of-the-art reconstruction performance on multiple public benchmarks while maintaining superior computational efficiency.

0 citationsRead paper

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

Mar 25, 2025

To address the high computational complexity and low inference efficiency of Transformers in light field (LF) image super-resolution—stemming from their self-attention mechanism—this paper proposes LF-VSSM, the first lightweight state space model tailored for LF super-resolution. LF-VSSM hierarchically models long-range dependencies: intra-view spatial, inter-view spatial-angular, and pixel-level spatial-angular correlations, marking the first systematic integration of state space models into LF super-resolution. Leveraging LF geometric priors and progressive feature extraction, the network achieves significant parameter and FLOPs reduction. Extensive experiments on multiple benchmark datasets demonstrate that LF-VSSM surpasses existing state-of-the-art methods in PSNR and SSIM while reducing model size and computational cost; notably, it achieves a 42% speedup in inference time.

0 citationsRead paper