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NightHaze: Nighttime Image Dehazing via Self-Prior Learning
The author introduces the MAE-like framework to enhance nighttime images by utilizing severe augmentation during training to develop strong network priors resilient to real-world night haze effects.
NightHaze: Nighttime Image Dehazing via Self-Prior Learning
Severe augmentation during training yields strong network priors for effective nighttime image dehazing.
A Comprehensive Study on Semi-supervised Nighttime Dehazing Baseline with Spatial-Frequency Awareness and Realistic Brightness Constraint
Proposing a semi-supervised model for real-world nighttime dehazing with spatial-frequency awareness and realistic brightness constraint.
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