PropMambaSR: Lightweight Image Super-Resolution With Propagation State Space Model

State Space Models (SSMs), particularly Mamba, have emerged as promising alternatives to Transformers for lightweight Single Image Super-Resolution (SISR) due to their linear complexity in modeling long-range dependencies. Advanced Mamba-based Super-Resolution models, such as MambaIR and VMambaIR, have demonstrated compelling performance by integrating bidirectional and multi-directional scanning mechanisms. However, these methods are still constrained by several critical challenges that limit their ultimate reconstruction capabilities. Firstly, the inherent requirement for 2D-to-1D serialization disrupts local spatial coherence, which is vital for preserving fine-grained texture details during reconstruction. Second, stacking multiple SSM blocks in deep networks leads to progressive decay of fine-grained information, as hidden states are typically confined within individual layers without cross-layer propagation. To address these challenges, we propose PropMambaSR, a novel hybrid architecture that synergistically combines global context modeling with local feature extraction. Our core contribution is the Propagation State Space Model (PropSSM), which establishes explicit cross-layer hidden state routing to enable direct propagation of fine-grained information from shallow to deep layers, effectively mitigating information decay while preserving reconstruction details. Additionally, we introduce an Enhanced Spatial Feature Block (ESFB) that employs multi-scale residual distillation to explicitly capture local texture information compromised by the serialization process. Finally, a dynamic Fusion Model (FM) adaptively integrates global contextual features from PropSSM with local spatial features from ESFB through learnable gating mechanisms. Extensive experiments demonstrate that PropMambaSR achieves state-of-the-art performance across multiple benchmarks while maintaining computational efficiency. Notably, on the Urban100 dataset for$\times 2$SR, our model surpasses the next-best method by 0.33 dB in PSNR, demonstrating superior capability in reconstructing high-frequency textures and regular structural patterns.