LFMamba: Focal Stack-aware State Space Modeling for Light Field Salient Object Detection

Salient object detection (SOD) in light field data presents unique challenges due to dynamic semantic inconsistencies across focal slices and representation heterogeneity between focal slices and the all-focus image. Existing methods often treat focal slices uniformly or rely on simple fusion strategies, which fail to address focus-induced semantic drift and cross-modal feature misalignment. To tackle these issues, we propose LFMamba, a unified network that jointly models dynamic semantic consistency and adaptive cross-modal fusion. We design the Focal-aware State Space Module (FSSM), which generates focal-aware semantic prompts through low-rank decomposition and adaptively routes them according to focal plane indices, thereby enabling bidirectional semantic propagation across slices through non-causal state transitions. Furthermore, we introduce the Focal-guided Cross-modal Fusion Module (FCFM), which mitigates cross-modal heterogeneity by a two-stage hierarchical strategy, combining structure-aware low-level alignment and gated high-level semantic fusion. Extensive experiments on four public light field SOD benchmarks demonstrate that LFMamba achieves superior performance compared to state-of-the-art methods, with improved robustness and consistency under complex focal variation scenarios.