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Shadow Removal by a Lightness-Guided Network With Training on Unpaired Data

机译:通过轻率引导的网络删除具有在未配对数据上的培训

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Shadow removal can significantly improve the image visual quality and has many applications in computer vision. Deep learning methods based on CNNs have become the most effective approach for shadow removal by training on either paired data, where both the shadow and underlying shadow-free versions of an image are known, or unpaired data, where shadow and shadow-free training images are totally different with no correspondence. In practice, CNN training on unpaired data is more preferred given the easiness of training data collection. In this paper, we present a new Lightness-Guided Shadow Removal Network (LG-ShadowNet) for shadow removal by training on unpaired data. In this method, we first train a CNN module to compensate for the lightness and then train a second CNN module with the guidance of lightness information from the first CNN module for final shadow removal. We also introduce a loss function to further utilise the colour prior of existing data. Extensive experiments on widely used ISTD, adjusted ISTD and USR datasets demonstrate that the proposed method outperforms the state-of-the-art methods with training on unpaired data.
机译:阴影拆卸可以显着提高图像视觉质量,并在计算机视觉中具有许多应用。基于CNN的深度学习方法已成为在任何配对数据上训练训练的最有效的暗影方法,其中图像的阴影和底层无影子版本是已知的或未配对的数据,其中阴影和无影子训练图像完全不同,没有通信。在实践中,考虑到培训数据收集的容易性,更优选对未配对数据的CNN培训。在本文中,我们通过对未配对数据进行训练展示了一种用于暗影移除的新的亮度引导阴影拆除网络(LG-ShadowNet)。在该方法中,我们首先训练CNN模块来补偿光照,然后从第一CNN模块中汲取第二个CNN模块,从第一CNN模块中汲取光照信息的指导。我们还引入损耗功能,以进一步利用现有数据之前的颜色。广泛使用的ISTD进行了广泛的实验,调整后的ISTD和USR数据集表明,所提出的方法优于现有技术的方法对未配对数据的培训。

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