Multi-Scale Adaptive Modulation Network for Efficient Image Super-Resolution

As convolutional neural networks (CNNs) become gradually larger and deeper, their applicability in real-time and resource-constrained environments is significantly limited. Furthermore, while self-attention (SA) mechanisms excel at capturing global dependencies, they often emphasize low-frequency information and struggle to represent fine local details. To overcome these limitations, we propose a multi-scale adaptive modulation network (MAMN) for image super-resolution. The MAMN mainly consists of a series of multi-scale adaptive modulation blocks (MAMBs), each of which incorporates a multi-scale adaptive modulation layer (MAML), a local detail extraction layer (LDEL), and two Swin Transformer Layers (STLs). The MAML is designed to capture multi-scale non-local representations, while the LDEL complements this by extracting high-frequency local features. Additionally, the STLs enhance long-range dependency modeling, effectively expanding the receptive field and integrating global contextual information. Extensive experiments demonstrate that the proposed method achieves an optimal trade-off between computational efficiency and reconstruction performance across five benchmark datasets.