计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 145-152.doi: 10.11896/jsjkx.200900109
石恒, 张玲
SHI Heng, ZHANG Ling
摘要： 虽然现有基于深度学习的图像阴影消除方法已取得了一定的进步,但是这些方法主要关注图像本身,没有很好地探索其他额外与阴影相关的信息,因此这些方法常常存在图像纹理模糊、内容不协调等问题。针对这些问题,文中基于生成对抗网络(Generative Adversarial Network,GAN),提出了一种新的阴影消除网络模型。该方法首先从全局上生成一个粗糙的阴影消除结果,再利用与阴影相关的残差信息对粗糙的结果在颜色和细节上进行局部优化,从而获得更加真实自然的无阴影图像。生成网络包含3个编码-解码结构,首先利用第1个编码-解码结构对阴影图像进行整体光照恢复,生成一个初始的阴影消除结果;同时将与阴影相关的残差信息作为辅助信息输入第2个编码-解码器,对初始结果进行进一步优化;为了避免阴影区域出现纹理不协调等问题,最后利用第3个编码-解码器对阴影区域细节纹理进行修正,使得生成的阴影消除图像更加真实自然。对抗网络由Patch鉴别器构成,用来鉴别图像阴影消除结果的真实性。实验结果表明,与目前的图像阴影消除方法相比,无论在阴影区域还是在非阴影区域上所提方法都达到了最佳的RMSE值,且该方法生成的阴影消除图像与真实无阴影图像更加接近,有效解决了纹理模糊等问题,证实了该方法的有效性和可行性。
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