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ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal

机译:ARGAN:细致的循环生成对抗网络,用于阴影检测和去除

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In this paper we propose an attentive recurrent generative adversarial network (ARGAN) to detect and remove shadows in an image. The generator consists of multiple progressive steps. At each step a shadow attention detector is firstly exploited to generate an attention map which specifies shadow regions in the input image. Given the attention map, a negative residual by a shadow remover encoder will recover a shadow-lighter or even a shadow-free image. The discriminator is designed to classify whether the output image in the last progressive step is real or fake. Moreover, ARGAN is suitable to be trained with a semi-supervised strategy to make full use of sufficient unsupervised data. The experiments on four public datasets have demonstrated that our ARGAN is robust to detect both simple and complex shadows and to produce more realistic shadow removal results. It outperforms the state-of-the-art methods, especially in detail of recovering shadow areas.
机译:在本文中,我们提出了一种细心的递归生成对抗网络(ARGAN),用于检测和去除图像中的阴影。生成器包含多个渐进步骤。在每一步骤,首先利用阴影注意检测器来生成注意图,该注意图指定输入图像中的阴影区域。给定注意力图,阴影去除编码器产生的负残差将恢复较亮的阴影甚至无阴影的图像。鉴别器旨在对最后一个渐进步骤中的输出图像是真实的还是伪造的进行分类。此外,ARGAN适合采用半监督策略进行培训,以充分利用足够的非监督数据。在四个公共数据集上进行的实验表明,我们的ARGAN具有检测简单和复杂阴影并产生更真实的阴影去除结果的强大功能。它优于最新的方法,尤其是在恢复阴影区域方面。

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