Hazy weather brings a lot of inconvenience to peoples lives, such as transportation. Therefore, image dehazing is still an important focus point. To achieve image dehazing, we proposed a Deep Dilated Residual Haze Network (DDRHNet) based on self-encoder and self-decoder. The proposed mode is an end-to-end supervised method, which directly estimates image dehazing result instead of estimating the atmospheric light and the transmission in the unsupervised and classical atmospheric scattering model. The DDRHNet method obtains image dehazed result by finding the difference between the input hazy image and the output of haze-free image. Experimental results on an open image dehazing dataset called SOTS demonstrate the superiority of the proposed DDRHNet network.
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Image Dehazing Network Based on Dilated Convolution Feature Extraction
Semantic Scholar · Computer Science · 2019
Abstract
Hazy weather brings a lot of inconvenience to peoples lives, such as transportation. Therefore, image dehazing is still an important focus point. To achieve image dehazing, we proposed a Deep Dilated Residual Haze Network (DDRHNet) based on self-encoder and self-decoder. The proposed mode is an end-to-end supervised method, which directly estimates image dehazing result instead of estimating the atmospheric light and the transmission in the unsupervised and classical atmospheric scattering model. The DDRHNet method obtains image dehazed result by finding the difference between the input hazy image and the output of haze-free image. Experimental results on an open image dehazing dataset called SOTS demonstrate the superiority of the proposed DDRHNet network.