Bridging Image Restoration and Recognition via Causal Mediated Unrolling

Robust visual recognition under degraded imaging conditions is critical for real-world vision systems.Task-oriented image restoration addresses this problem by enhancing degraded inputs before recognition, but it must preserve natural visual appearance without disrupting the evidence used by downstream recognizers.Existing optimization strategies struggle to satisfy these requirements simultaneously.Restoration driven mainly by visual quality may weaken task-critical cues or introduce texture shifts that are visually plausible but poorly matched to the recognizer.Directly tuning restoration networks with recognition loss is also insufficient, as the restoration model may exploit recognizer-specific shortcut cues and produce visually unnatural artifacts while improving task scores.We propose Causal Mediated Unrolling (CaMeU), an optimization-based framework for task-oriented image restoration with frozen recognition models.Starting from a joint optimization objective, the proposed framework derives a Half-Quadratic Splitting formulation that separates restoration into alternating task-driven and prior-guided updates.The task-driven update introduces an explicit mediator for front-door guided optimization, reducing shortcut bias induced by the recognizer.The prior-guided update performs manifold projection, pulling the intermediate result back toward the natural-image manifold to preserve visual quality.Through this mediated alternating process, the restored image is progressively optimized for both recognition performance and visual quality.Experiments on VOC Haze, VOC Dark, and degraded CUB-200-2011 show that CaMeU consistently improves downstream recognition while reducing visual artifacts.