Beyond Synthetic: Real-world Post-Training for Image Restoration with Imperfect Generative Ground Truth

Generative image restoration has achieved impressive performance, but models trained solely on synthetic data struggle to generalize to real-world scenarios. We propose a degradation-based pre-training and post-training paradigm, in which post-training data are constructed by generating ground-truth for real low-quality (LQ) images using image editing models. However, generated targets usually contain hallucinations, making them unreliable for direct supervision. Our key insight is that effective degradation adaptation does not require every region of a generated target to be fully reliable. Based on this observation, we develop a simple yet effective approach that employs a vision-language model to localize hallucinated regions and masks their gradients during post-training. A localized refinement and verification loop further refines these regions, thereby recovering additional usable supervision. Experiments on old-photo enhancement, user-generated content enhancement, and real-world super-resolution across three representative generative restoration models demonstrate that the proposed framework consistently improves restoration performance in specific real-world scenarios.