All-in-One Weather-Degraded Image Restoration Via Adaptive Degradation-Aware Self-Prompting Model

Existing approaches for all-in-one weather-degraded image restoration suffer from inefficiencies in leveraging degradation-aware priors, resulting in sub-optimal performance in adapting to different weather conditions. To this end, we develop an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weather-degraded image restoration. Specifically, our model employs the contrastive language-image pre-training model (CLIP) to facilitate the training of our proposed latent prompt generators (LPGs), which represent three types of latent prompts to characterize the degradation type, degradation property and image caption. Moreover, we integrate the acquired degradation-aware prompts into the time…

• NN
  National Natural Science Foundation of China

  Award: 52172379
• FR
  Fundamental Research Funds for the Central Universities

  Award: 300102242901