Subband-Guided Hybrid Multi-Axis Attention Network for Frequency-Aware Image Super-Resolution
Single-image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) observations while preserving structural information and high-frequency detail. Although the Hybrid Multi-Axis Network (HMA) effectively combines local and nonlocal attention, its shallow input representation still mixes low-frequency structure, directional detail, and noise-like high-frequency components. This study investigates whether an explicit frequency prior can be introduced before the HMA backbone without substantially increasing computational cost. Two discrete-wavelet-transform front-ends are examined under the ×2 setting. HMA-WSB uses lightweight subband-specific processing and weighted fusion before a shared HMA backbone, whereas HMA-MSB introduces asymmetric multi-subband branches and cross-band fusion. Evaluation includes the reported external 15-image experiment, selected-image pilots from Set5, Set14, BSD100, and Urban100, and supplementary medical and texture-domain samples. The results show small, content-dependent differences rather than a consistent reconstruction advantage: the proposed variants are slightly favorable on several images containing dense multidirectional detail, but the original HMA remains stronger on other natural, medical, and periodic-texture samples. Computational analysis on an NVIDIA GeForce RTX 5070 with a 64×64 low-resolution input shows that HMA-WSB increases measured inference latency by 1.637% with negligible parameter and memory overhead. HMA-MSB increases latency by 5.078%, parameter count by 1.463%, and estimated FLOPs by 0.489%. These findings indicate that wavelet-guided subband processing is compatible with HMA and that WSB provides the more computationally economical extension. However, because the standard-dataset evaluation is based on selected images and a complete component-level ablation is not available, the results should be interpreted as preliminary evidence of a content-dependent quality-cost trade-off rather than proof of broad superiority.