Coupling-Aware Compositional Restoration: Zero-Shot, Training-Free Removal of Composite Adverse Weather

Real-world adverse weather rarely appears in isolation: low light, haze, rain, and snow frequently co-occur and interact, producing composite degradations that are far harder to remove than any single effect. State-of-the-art methods for composite weather are fully supervised and must be trained on a combinatorially large set of degradation combinations, while existing training-free restorers built on diffusion priors are designed for a single, known degradation and break down once degradations are stacked. We present CACR, a zero-shot, training-free framework for composite adverse-weather restoration. CACR detects which base weathers are present, derives a physically consistent removal order, and applies image-space physical operators in a coupling-aware manner, re-estimating each operator’s parameters from the partially restored image rather than from the raw input. Restoration is performed entirely by zero-shot classical operators: no restoration network is trained, and no paired restoration data is used. Weather detection itself runs zero-shot from physical cues, with an optional lightweight few-shot probe that only sharpens detection, never the restoration. We show that single-degradation zero-shot restorers can fall below the degraded input on stacked weather, that the coupling, not the individual operators, drives our improvement, and that diffusion-inversion editing is bounded by a reconstruction ceiling that motivates restoring in image space. On the CDD-11 composite-weather benchmark, CACR provides the first zero-shot, training-free reference point across all eleven classes, reported in PSNR, SSIM, and LPIPS, with a clear characterization of the remaining gap to supervised state of the art.