Fixing the Last Mile: Failure-Aware Image Restoration with Region-Specific Guidance

Recent advances in image restoration have greatly improved global image fidelity, producing visually plausible results across most regions of an image. Nevertheless, even high-quality restorations often exhibit localized failures, including artifacts in semantically important regions such as faces and text, structural inconsistencies, and texture mismatches. Although these errors typically affect only a small portion of the image, they often dominate the overall perceptual quality of the result. In this work, we propose **Failure-Aware Image Restoration (FAIR)** to address the **“last-mile”** problem in image restoration. Rather than applying one uniform generative prior over the whole image, we condition a single-pass restoration model on both **where** local failures are likely to occur and **what** corrective guidance each region requires. This region-bound conditioning enables fine-grained and controllable restoration that improves semantic accuracy and stylistic consistency in critical regions while maintaining faithful, high-quality restoration elsewhere. Our method complements existing restoration pipelines by shifting the emphasis from global fidelity to local perceptual acceptability. Extensive experiments show that the proposed approach resolves common failure cases and improves perceptual quality, bridging the gap between technical accuracy and practical usability.