Single-image super-resolution has progressed from deep convolutional baselines to stronger Transformer and state-space architectures, yet the corresponding performance gains typically come with higher training cost, longer engineering iteration, and heavier deployment burden. In many practical settings, multiple pretrained models with partially complementary behaviors are already available, and the binding constraint is no longer architectural capacity but how effectively their outputs can be combined without additional training. Rather than pursuing further architectural redesign, this paper proposes a training-free output-level ensemble framework. A dual-branch pipeline is constructed in which a Hybrid attention network with TLC inference provides stable main reconstruction, while a MambaIRv2 branch with geometric self-ensemble supplies strong compensation for high-frequency detail recovery. The two branches process the same low-resolution input independently and are fused in the image space via a lightweight weighted combination, without updating any model parameters or introducing an additional trainable module. As our solution to the NTIRE 2026 Image Super-Resolution ($\times 4$) Challenge, the proposed design consistently improves over the base branch and slightly exceeds the pure strong branch in PSNR at the best operating point under a unified DIV2K bicubic $\times 4$ evaluation protocol. Ablation studies confirm that output-level compensation provides a low-overhead and practically accessible upgrade path for existing super-resolution systems.

翻译：单图像超分辨率已从深度卷积基线模型发展到更强的Transformer和状态空间架构，但相应的性能提升通常伴随着更高的训练成本、更长的工程迭代周期以及更重的部署负担。在许多实际场景中，多个已预训练的模型具有部分互补的行为特征，此时制约因素不再是架构容量，而是如何在不额外训练的情况下有效组合这些模型的输出。本文不再追求进一步的架构重新设计，而是提出一种无训练的输出级集成框架。我们构建了一个双支路管线：其中混合注意力网络结合TLC推理提供稳定的主重构，而具有几何自集成的MambaIRv2支路则提供高频细节恢复的强补偿。两条支路独立处理相同的低分辨率输入，并通过轻量级加权组合在图像空间进行融合，无需更新任何模型参数或引入额外可训练模块。作为我们对NTIRE 2026图像超分辨率（×4）挑战赛的解决方案，所提出的设计在统一的DIV2K双三次×4评估协议下，始终优于基础支路，并在最优工作点上略超纯强支路的PSNR峰值信噪比。消融研究证实，输出级补偿为现有超分辨率系统提供了一条低开销且易于实践的性能升级路径。