URNet: A U-Net based residual network for image dehazing

Low visibility in hazy weather causes the loss of image details in digital images captured by some imaging devices such as monitors. This paper proposes an end-to-end U-Net based residual network (URNet) to improve the visibility of hazy images. The encoder module of URNet uses hybrid convolution combining standard convolution with dilated convolution to expand the receptive field for extracting image features with more details. The URNet embeds several building blocks of ResNet into the junction between the encoder module and the decoder module. This prevents network performance degradation due to the vanishing gradient. After considering large absolute difference on image saturation and value components between hazy images and haze-free images in the HSV color space, the URNet defines a new loss function to better guide the network training. Experimental results on synthetic hazy images and real hazy images show that the URNet significantly improves the image dehazing effect compared to the state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes an end-to-end U-Net based residual network to enhance visibility of hazy images, which utilizes hybrid convolution and ResNet building blocks to prevent gradient vanishing, leading to significant improvement in image dehazing effect.