Frequency Matters: Frequency-Aware Decoupling and Harmonized Enhancement for High-Fidelity Medical Image Super-Resolution

Single-image super-resolution (SISR) in medical imaging is hindered by the low-frequency bias of deep learning and the distinct semantic roles of mid- and high-frequency components, which uniform enhancement strategies fail to address. We propose the Frequency-Aware Representation Enhancer (FARE), a plug-and-play module that explicitly decouples and enhances these frequency bands. FARE comprises a High-Frequency Semantic Amplifier (HFSA), which combines directional wavelet decomposition with gradient-informed gating, and an Adaptive Mid-Frequency Decoupler (AMFD), which applies patch-level STFT with a learnable Gaussian band-pass mask. Their outputs are fused via cross-frequency negotiated gating. Extensive evaluations on multiple medical image benchmarks demonstrate consistent improvements over state-of-the-art methods.