Frequency-Aware Bidirectional Interactive Mamba Network for Image Super-Resolution

Although state space models (SSMs) represented by Mamba have achieved long-range dependency modeling with linear complexity, they still struggle to realize differentiated processing of high-frequency and low-frequency components in image super-resolution (SR) tasks. To address the drawbacks of existing wavelet-Mamba methods, such as texture distortion and unstable training caused by independent frequency band modeling, this paper proposes a frequency-aware bidirectional interactive mamba network (FABIMNet). The network uses discrete wavelet transform to decouple image frequency-domain components. The core lies in the proposed bidirectional high-frequency enhancement (Bi-HFE) module, which constructs an interaction mechanism of low-frequency guiding high-frequency generation and high-frequency feedback correcting low-frequency structure, and cooperates with the lightweight cross-band interaction (LCBI) module to achieve information synchronization and complementarity during deep feature extraction. Extensive experiments demonstrate that the proposed method achieves a competitive trade-off between computational efficiency and reconstruction performance across five benchmark datasets.