基于衰减式生成对抗网络的单幅图像阴影去除

图像中的阴影是投影物体的重要视觉信息，但也会对计算机视觉任务造成影响。现有的单幅图像阴影去除方法因鲁棒阴影特征的缺乏或训练样本数据的不足与误差等原因，无法得到好的阴影去除结果。为了准确生成用于描述阴影区域光照衰减程度的蒙版图像，进而获得高质量的无阴影图像，提出了一种基于衰减式生成对抗网络的单幅图像阴影去除方法。首先，敏感因子引导的衰减器被用来提升训练样本数据，为后续的生成器与判别器提供符合物理光照模型的阴影样本图像。其次，生成器将结合感知损失，并在判别器的督促下得到最终阴影蒙版。与相关研究工作比较，所提方法能有效恢复阴影区域的光照信息，可以得到更为逼真、阴影边界过渡更加自然的无阴影图像。利用客观指标评价阴影去除结果。实验结果表明，该方法能在多个真实场景下有效去除阴影，去阴影结果视觉一致性良好。

Single image shadow removal based on attenuated generative adversarial networks

Shadow in an image is important visual information of the projective object, but it affects computer vision tasks. Existing single image shadow removal methods cannot obtain good shadow-free results due to the lack of robust shadow features or insufficiency of and errors in training sample data. In order to generate accurately the shadow mask image for describing the illumination attenuation degree and obtain the high quality shadow-free image, a single image shadow removal method based on attenuated generative adversarial network was proposed. Firstly, an attenuator guided by the sensitive parameters was used to augment the training sample data in order to provide shadow sample images agreed with physical illumination model for a subsequent generator and discriminator. Then, with the supervision from the discriminator, the generator combined perceptual loss function to generate the final shadow mask. Compared with related works, the proposed method can effectively recover the illumination information of shadow regions and obtain the more realistic shadow-free image with natural transition of shadow boundary. Shadow removal results were evaluated using objective metric. Experimental results show that the proposed method can remove shadow effectively in various real scenes with a good visual consistency.