Controlling vision-language model for enhancing image restoration

Restoring low-quality images to their original high-quality state remains a significant challenge due to inherent uncertainties, particularly in blind image restoration scenarios where the nature of degradation is unknown. Despite recent advances, many restoration techniques still grapple with robustness and adaptability across diverse degradation conditions. In this paper, we introduce an approach to augment the restoration model by exploiting the robust prior features of CLIP, a large-scale vision-language model, to enhance its proficiency in handling a broader spectrum of degradation tasks. We integrate the robust priors from CLIP into the pre-trained image restoration model via cross-attention mechanisms, and we design a Prior Adapter to modulate these features, thereby enhancing the model’s restoration performance. Additionally, we introduce an innovative prompt learning framework that harnesses CLIP’s multimodal alignment capabilities to fine-tune pre-trained restoration models. Furthermore, we utilize CLIP’s contrastive loss to ensure that the restored images align more closely with the prompts of clean images in CLIP’s latent space, thereby improving the quality of the restoration. Through comprehensive experiments, we demonstrate the effectiveness and robustness of our method, showcasing its superior adaptability to a wide array of degradation tasks. Our findings emphasize the potential of integrating vision-language models such as CLIP to advance the cutting-edge in image restoration.