MFET: Multi-frequency enhancement transformer for single-image super-resolution

Single-Image Super-Resolution (SISR) aims to reconstruct a high-resolution image from a low-resolution input while effectively preserving structural integrity and fine details. However, (i) low-frequency structural cues progressively fade during deep-layer propagation, and (ii) existing upsampling modules either ignore multi-scale context or incur excessive computation, leading to unsatisfactory high-frequency texture recovery. To address these limitations, we propose the Multi-Frequency Enhancement Transformer (MFET), a novel Transformer-based network tailored for efficient SISR. MFET seamlessly integrates low-frequency structural preservation with high-frequency detail recovery through its Multi-Frequency Block (MFB). The MFB employs a Residual Attention Mechanism (RAM) to propagate fine-grained features across layers, ensuring robust retention of low-level details, and an Efficient Upscale Module (EUM) with a pyramidal structure and depthwise separable convolutions to enhance high-frequency components with minimal computational cost. Extensive experiments on benchmark datasets demonstrate that MFET achieves superior performance in PSNR and SSIM, particularly at ×3 and ×4 scales, excelling in texture and edge reconstruction. MFET strikes an optimal balance between quality and efficiency, offering a promising solution for high-quality super-resolution. Our code is available at https://github.com/snh4/MFET.