CONTROLIR: LEARNING TO CONTROL STATE SPACE MODELS VIA SPATIAL EVIDENCE FOR IMAGE RESTORATION

Image restoration aims to remove degradations and reconstruct clear scene content, but complex scene structures and spatially varying degradations make it difficult to suppress interference while preserving details. Existing methods use scene structure or degradation cues to guide restoration, but how to jointly describe the structural and degradation properties of different regions and use them to regulate the restoration process remains a key challenge. We propose ControlIR, a compact restoration network that extracts multidimensional spatial evidence from the input image to describe regional scene structure and degradation characteristics while preserving the spatial correspondence among evidence fields. It then uses these fields to condition the state-space restoration process according to their roles, allowing each location to adapt how it uses cross-region information while removing local interference and preserving both fine details and overall structure. Experiments show that ControlIR achieves higher image restoration quality than the compared methods. Controlled studies verify the contributions of multidimensional spatial evidence and operation-level control while requiring only small increases in parameters and computation.