Abstract
Hybrid CNN-Transformer architectures achieve strong results in image super-resolution, but scaling attention windows or convolution kernels significantly increases computational cost, limiting deployment on resource-constrained devices. We present UCAN, a lightweight network that unifies convolution and attention to expand the effective receptive field efficiently. UCAN combines window-based spatial attention with a Hedgehog Attention mechanism to model both local texture and long-range dependencies, and introduces a distillation-based large-kernel module to preserve high-frequency structure without heavy computation. In addition, we employ cross-layer parameter sharing to further reduce complexity. On Manga109 ($4\times$), UCAN-L achieves 31.63 dB PSNR with only 48.4G MACs, surpassing recent lightweight models. On BSDS100, UCAN attains 27.79 dB, outperforming methods with significantly larger models. Extensive experiments show that UCAN achieves a superior trade-off between accuracy, efficiency, and scalability, making it well-suited for practical high-resolution image restoration.
Abstract (translated)
混合CNN-Transformer架构在图像超分辨率任务中取得了很好的结果，但扩大注意力窗口或卷积核会显著增加计算成本，从而限制了其在资源受限设备上的部署。我们提出了UCAN（Unified Convolution and Attention Network），这是一种轻量级网络，它将卷积和注意力统一起来以高效地扩展有效感受野。 UCAN结合了基于窗口的空间注意机制与刺猬注意力机制，以便同时建模局部纹理和长程依赖关系，并引入了一种基于知识蒸馏的大核模块来在不进行大量计算的情况下保留高频结构。此外，我们还采用了跨层参数共享策略进一步降低复杂度。 在Manga109（4倍超分辨率）数据集上，UCAN-L实现了31.63 dB的PSNR值，仅使用了48.4G MACs的操作量，超过了最近的一些轻量级模型。而在BSDS100数据集上，UCAN达到了27.79 dB的成绩，优于那些拥有显著更大规模模型的方法。 通过广泛的实验显示，UCAN在精度、效率和可扩展性之间实现了更佳的平衡，使其非常适合用于实际高分辨率图像恢复任务中。
URL
https://arxiv.org/abs/2603.11680
