Liu Zhihao, Yin Hui, Mi Yang, Pu Mengyang, Wang Song
IEEE Trans Image Process. 2021;30:1853-1865. doi: 10.1109/TIP.2020.3048677. Epub 2021 Jan 18.
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.
阴影去除可以显著提高图像视觉质量，并且在计算机视觉中有许多应用。基于卷积神经网络（CNNs）的深度学习方法已成为通过在成对数据（即图像的阴影和潜在无阴影版本均已知）或不成对数据（即阴影和无阴影训练图像完全不同且无对应关系）上进行训练来进行阴影去除的最有效方法。在实践中，鉴于训练数据收集的简便性，在不成对数据上进行卷积神经网络训练更受青睐。在本文中，我们提出了一种新的亮度引导阴影去除网络（LG-ShadowNet），用于在不成对数据上进行训练以去除阴影。在该方法中，我们首先训练一个卷积神经网络模块来补偿亮度，然后在第一个卷积神经网络模块的亮度信息指导下训练第二个卷积神经网络模块以进行最终的阴影去除。我们还引入了一个损失函数来进一步利用现有数据的颜色先验。在广泛使用的ISTD、调整后的ISTD和USR数据集上进行的大量实验表明，所提出的方法在不成对数据训练方面优于现有最先进的方法。
