Conceitos essenciais
Severe augmentation during training yields strong network priors for effective nighttime image dehazing.
Resumo
The paper introduces NightHaze, a novel nighttime image dehazing method with self-prior learning. Severe augmentation is used to enhance visibility by blending clear images with light effects and noise. The self-refinement module further refines the output to address artifacts like over-suppression. Extensive experiments show significant performance improvement over existing methods.
- Introduction to nighttime image dehazing challenges.
- Proposal of NightHaze with self-prior learning and severe augmentation.
- Explanation of the self-refinement module for artifact reduction.
- Comparison of results with state-of-the-art methods.
- Ablation studies on the effectiveness of self-prior learning and self-refinement.
Estatísticas
MAE masks 75% of input images before reconstructing them.
NightHaze achieves a performance margin of 15.5% for MUSIQ and 23.5% for ClipIQA.
Citações
"Severe augmentation during training produces strong network priors that are resilient to real-world night haze degradations."
"Our main novelty lies in defining 'severe' based on proper augmentation."