Learning Instance-Adaptive Coupled-Axis Relation Prompts for All-in-One Image Restoration

All-in-one image restoration aims to recover images corrupted by unknown and heterogeneous degradations using a single model. Existing methods primarily adapt shared features or parameters through prompts and degradation representations, while leaving the underlying attention interactions generic and often dense. This separation between degradation conditioning and relation modeling provides no structured carrier for expressing instance-specific relation changes. We propose InCaRP, an all-in-one restoration Transformer that learns instance-adaptive coupled-axis relation prompts. Specifically, low-rank relation-space attention performs structured sparsification before correlation by projecting redundant key and value responses directly into a compact set of input-dependent relation carriers, while preserving the full query and output representations. Coupled-axis prompt calibration then uses shared angular retrieval to compose complementary query-axis and relation-axis prompts from paired banks. Normalized interactions between the two prompts synthesize bounded scale and shift fields that calibrate the compact relations before value aggregation. The unified design therefore combines pre-correlation information compression with instance-adaptive relation calibration. Extensive experiments on all-in-one benchmarks demonstrate that our method achieves state-of-the-art performance while maintaining parameter efficiency and shows strong, balanced performance across heterogeneous restoration tasks.