Using Haze Level Estimation in Data Cleaning for Supervised Deep Image Dehazing Models
Cheng-Hsiung Hsieh,
Ze-Yu Chen
Abstract:Recently, supervised deep learning methods have been widely used for image haze removal. These methods rely on training data that are assumed to be appropriate. However, this assumption may not always be true. We observe that some data may contain hazy ground truth (GT) images. This can lead to supervised deep image dehazing (SDID) models learning inappropriate mapping between hazy images and GT images, which negatively affects the dehazing performance. To address this problem, two difficulties must be solved.… Show more
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