Abstract
The current single image rain removal method is not ideal in the task of rain and haze removal. In this paper, based on the physical imaging mechanism under heavy rain conditions, a feature enhanced composition model, FEC-Net, is constructed by combining physical model-driven and data-driven methods. The feature maps, which contain rain streaks, atmospheric light, and transmission map information were obtained by three convolution layers. These feature maps are enhanced by the feature enhancement module. Finally, a clear image is obtained according to the formula of the atmospheric scattering model. The selective enhanced processing of key output data greatly improves the processing effect of the network. Extensive experiments show that our FEC-Net achieves better accuracy and visual improvements against state-of-the-art methods.
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