Frequency-Aware Adaptive Fusion Gate for Single Image Super-Resolution

The Dense-Residual-Connected Transformer (DRCT) has established a new state-of-the-art in single image super-resolution by mitigating the information bottleneck in deep networks. However, its feature aggregation mechanism relies on a suboptimal Static Addition strategy, where residual features are scaled by a fixed, learnable scalar, regardless of the image content. This content-agnostic approach treats high-frequency textures and low-frequency noise indiscriminately, limiting the model’s representational capability. To address this, we propose a Frequency-Aware Adaptive Fusion Gate (FAFG) to replace the static scaling. Unlike spatial-only gating mechanisms, FAFG integrates the Discrete Cosine Transform (DCT) to explicitly perceive the frequency distribution of feature maps. By decomposing features into frequency components, our gate acts as an intelligent valve, dynamically amplifying valid structural details while suppressing redundant background noise. Extensive experiments on standard benchmarks demonstrate that our proposed FAFG-integrated model consistently outperforms the static-scaling and other state-of-the-art methods. Specifically, our method achieves a significant PSNR improvement of 0.31dB on the texture-rich Urban100 dataset at ×4 scale. Visual results further confirm that our frequency-aware gating mechanism effectively recovers more sharp edges and fine textures, providing a superior trade-off between reconstruction accuracy and model complexity.