Graphene: A Lightweight Transformer with Ising Mean-Field Refinement for Image Super-Resolution

This paper proposes *Graphene*, a lightweight image super-resolution architecture with two complementary feature refinement mechanisms: Ising-model-inspired local feature refinement using an *Ising Refinement Block* (IRB) and stage-history-based feature refinement using a *History Attention* (HistAttn) module. The IRB reuses attention output features and pre-softmax attention logits to construct signed pairwise token couplings within each local window, applies unrolled mean-field updates, and refines features through a gated residual correction. These signed couplings allow alignment and opposition between latent spins to modulate local feature refinement. HistAttn attends over the current stage output and earlier representations at the same token position and adaptively blends the current stage output with a history-weighted feature aggregate without adding spatial pairwise attention. For *classical SR* and *lightweight SR* on Urban100 at $\times 4$, Graphene and Graphene-light outperform the state-of-the-art methods IET and IET-light by **0.24 dB** and **0.38 dB**, respectively, with **34.3%/13.7% fewer parameters**, **44.8%/44.4% fewer FLOPs**, **87.9%/84.5% lower latency**, and **78.3%/87.5% lower peak memory**. *Real-world SR* comparisons further show that Graphene recovers fine details and preserves textures across diverse degradations. If the paper is accepted, the code will be released by the camera-ready deadline.