Morphology-Guided Sparse State-Space Routing for SAR Image Restoration

Radio-frequency interference corrupts synthetic aperture radar (SAR) echoes and produces structured artifacts during coherent focusing, making pre-focusing recovery important for SAR image restoration (SAR-IR). However, fixed state-space scan geometries limit adaptation to diverse interference morphologies, while dense multi-directional scanning incurs redundant computation. We propose MorphRoute-Mamba, a time-frequency SAR-IR framework that learns morphology-guided routing over predefined directional state-space experts. Guided by full record spectral context and Fast-SCNN-predicted interference masks, the framework selects a single expert per block and adaptively fuses its output with context from reliability-ranked anchors. This combines directional state-space propagation with reliability-guided contextual access. For efficient deployment, validation-guided route compilation fixes block-wise scan choices while retaining input-dependent conditioning and context fusion. Simulated experiments demonstrate reconstruction gains across the time-frequency, echo, and focused image domains, while measured SAR scenes provide complementary evidence of interference suppression and structural preservation. Component ablations support the proposed design, and deployment profiling demonstrates a favorable reconstruction-latency trade-off.