CURE: Clean-Guided Understanding Restoration for Degraded Images

Image restoration aims to recover high-quality images from degraded observations, providing reliable inputs for downstream visual tasks. When these tasks are performed by large vision-language models (LVLMs), conventional restore-then-understand pipelines still optimize restoration primarily for pixel fidelity or perceptual quality, which does not directly guarantee improvements in understanding and may propagate restoration errors into model responses. To address this issue, we propose understanding restoration for degraded inputs and introduce CURE (Clean-Guided Understanding REstoration). CURE first lets the model generate responses to degraded images, then uses its predictive distributions under clean-image conditions to supervise each step of the generated responses. This supervision guides the model’s generation decisions toward those under clean conditions, aiming to restore visual understanding impaired by image degradation. During inference, the adapted model directly processes degraded images without an additional image restoration module. On Qwen3.5-4B, CURE improves the average score on the real-world degradation benchmark R-Bench from 63.49 to 67.38, outperforming the evaluated two-stage restoration pipelines. It also achieves overall improvements in cross-dataset evaluations with synthetic degradations and evaluations on clean images. Furthermore, CURE can complement image restoration models in a two-stage pipeline to improve understanding of degraded images.