Recent image inpainting methods show promising results due to the power of deep learning, which can explore external information available from a large training dataset. However, many state-of-the-art inpainting networks are still limited in exploiting internal information available in the given input image at test time. To mitigate this problem, we present a novel and efficient self-supervised fine-tuning algorithm that can adapt the parameters of fully pre-trained inpainting networks without using ground-truth target images. We update the parameters of the pre-trained state-of-the-art inpainting networks by utilizing existing self-similar patches within the given input image without changing network architecture and improve the inpainting quality by a large margin. Qualitative and quantitative experimental results demonstrate the superiority of the proposed algorithm, and we achieve state-of-the-art inpainting results on publicly available numerous benchmark datasets.
翻译:由于深层学习的力量,最近的图像油漆方法显示出有希望的结果,因为深层学习可以探索从大型培训数据集获得的外部信息。然而,许多最先进的油漆网络在利用测试时特定输入图像中的现有内部信息方面仍然有限。为了缓解这一问题,我们提出了一个新颖而高效的自我监督的微调算法,可以在不使用地面实况目标图像的情况下调整经过充分预先训练的油漆网络的参数。我们通过在不改变网络结构的情况下利用特定输入图像中现有的自相类似的部分来更新培训前最新油漆网络的参数,同时不改变网络结构,提高大差值的油漆质量。定性和定量实验结果显示了拟议算法的优越性,我们在公开提供的众多基准数据集上实现了最先进的油漆结果。