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• W3111785512 endingPage "106884" @default.
• W3111785512 startingPage "106884" @default.
• W3111785512 abstract "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." @default.
• W3111785512 created "2020-12-21" @default.
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• W3111785512 date "2021-04-01" @default.
• W3111785512 modified "2023-10-14" @default.
• W3111785512 title "URNet: A U-Net based residual network for image dehazing" @default.
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• W3111785512 doi "https://doi.org/10.1016/j.asoc.2020.106884" @default.
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