An Image Dehazing Algorithm Based on Generative Adversarial Networks
In view of the issues such as color distortion,loss of texture details and grid artifacts in the images processed by the existing dehazing algorithms,an end-to-end dehazing algorithm based on generative adversarial networks is proposed.In this algorithm,U-Net is introduced into the generator module,which utilizes multi-scale convolution and skip connections to combine the characteristics of different levels.A hybrid dilated convolution module is employed to capture contextual information,expand the receptive field and alleviate the grid artifacts.Moreover,a composite loss function is utilized to constrain the image boundaries,thereby enhancing the fine quality of the dehazed images and effectively addressing the issues encountered by existing dehazing algorithms.Experimental results of the proposed algorithm on the SOTS data set show that both the objective evaluation metrics and the perceptual quality of the dehazed images outperform all the other compared algorithms.Additionally,experiments on the UA-DETARC data set confirm that the images processed by the proposed algorithm can be applied to the object detection tasks in traffic scenes.
image dehazinggenerative adversarial networkshybrid dilated convolutioncomposite loss functionob-ject detection
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