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Remove haze in a single image by using three components

Remove haze in a single image by using three components


From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data



Single image dehazing is a challenging task, for which the domain shift between synthetic training data and real-world testing images usually leads to degradation of existing methods.


... propose a novel image dehazing framework collaborating with unlabeled real data.


... develop a disentangled image dehazing network (DID-Net), which disentangles the feature representations into three component maps, i.e.


the latent haze-free image,

the transmission map, and

the global atmospheric light estimate, respecting the physical model of a haze process.


... make comparison with 13 state-of-the-art dehazing methods


... method has obvious quantitative and qualitative improvements over the existing methods.



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