Advancing Real-world Image Dehazing: A Comprehensive Dataset and Evaluation Metrics
Luigi Celona,
Flavio Piccoli
Abstract:The majority of dehazing techniques in the literature have been designed to learn from supervised datasets, which typically comprise pairs of images, one with haze and one without haze, to facilitate the learning process. These datasets often contain synthesized hazy images, either created by leveraging the theoretical model of haze creation, or by exploiting professional haze generation machines.
However, the lack of realism given by these generative processes has led to a limited capacity of dehazing techniq… Show more
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