FI-CRNet: Frequency Interaction for Cloud Removal in Remote Sensing Images

Remote sensing imagery is often degraded by cloud cover, causing severe information loss and hindering downstream Earth observation tasks. Although recent deep learning methods, including CNN- and Transformer-based models, have achieved promising progress in cloud removal, they mainly operate in the spatial domain and largely overlook the frequency-domain discrepancies introduced by clouds of different types and densities. This limitation restricts their ability to generalize across diverse cloud corruption scenarios. To address this issue, we propose a Frequency Interaction Cloud Removal Network (FI-CRNet), which introduces a novel Frequency-Aware Modulation (FAM) mechanism for high-fidelity cloud-free image reconstruction. The FAM module consists of two components. First, the Frequency Decomposition (FD) module explicitly separates input features into low-frequency cloud-affected components and high-frequency detail-rich components through spectral analysis, while aligning them with decoder features via cross-attention. Second, the Cross-Frequency Interaction (CFI) module adaptively integrates these components through a dual-gate weighting mechanism, including spatial and channel gates, to suppress cloud interference while enhancing structural and textural details. By jointly modeling frequency-domain cues and spatial features, FI-CRNet enables robust and adaptive reconstruction under diverse cloud conditions. Extensive experiments show that our method outperforms state-of-the-art techniques across diverse cloud scenarios.