论文标题
通过轻度引导网络去除阴影，并对未配对数据进行培训
Shadow Removal by a Lightness-Guided Network with Training on Unpaired Data
论文作者
论文摘要
删除阴影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的深度学习方法已通过对任何一个配对数据进行训练，成为最有效的删除阴影的方法，在该数据中，图像的阴影和潜在的无阴影版本都是已知的，或者是未配对的数据，其中没有阴影和无阴影的训练图像完全不同，没有信件。实际上，鉴于培训数据收集的易感性，CNN对未配对数据的培训更为首选。在本文中，我们提出了一个新的轻度引导的阴影去除网络（LG-Shadownet），以通过培训未配对的数据来删除阴影。在这种方法中，我们首先训练一个CNN模块以补偿轻度，然后训练第二个CNN模块，并通过第一个CNN模块的轻度信息进行指导以删除最终阴影。我们还引入了损失函数，以进一步利用现有数据的颜色。对广泛使用的ISTD，调整后的ISTD和USR数据集进行的广泛实验表明，通过对未配对数据进行培训，所提出的方法优于最先进的方法。
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.
