Using Haze Level Estimation in Data Cleaning for Supervised Deep Image Dehazing Models

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. One is to estimate the haze level in an image, and the other is to develop a haze level indicator to discriminate clear and hazy images. To this end, we proposed a haze level estimation (HLE) scheme based on dark channel prior and a haze level indicator accordingly for training data cleaning, i.e., to exclude image pairs with hazy GT images in the data set. With the data cleaning by the HLE, we introduced an SDID framework to avoid inappropriate learning and thus improve the dehazing performance. To verify the framework, using the RESIDE data set, experiments were conducted with three types of SDID models, i.e., GCAN, REFN and cGAN. The results show that our method can significantly improve the dehazing performance of the three SDID models. Subjectively, the proposed method generally provides better visual quality. Objectively, our method, using fewer training image pairs, was capable of improving PSNR in the GCAN, REFN, and cGAN models by 3.10 dB, 5.74 dB, and 6.44 dB, respectively. Furthermore, our method was evaluated using a real-world data set, KeDeMa. The results indicate that the better visual quality of the dehazed images is generally for models with the proposed data cleaning scheme. The results demonstrate that the proposed method effectively and efficiently enhances the dehazing performance in the given examples. The practical significance of this research is to provide an easy but effective way, that is, the proposed data cleaning scheme, to improve the performance of SDID models.