论文标题
边缘信息和掩模基于缩小的图像介入方法
An Edge Information and Mask Shrinking Based Image Inpainting Approach
论文作者
论文摘要
在图像介绍任务中，缺失区域中修复高频和低频信息的能力对恢复图像的质量具有很大的影响。但是，现有的灌溉方法通常无法同时考虑高频和低频信息。为了解决这个问题，本文提出了边缘信息，并掩盖了基于缩小的图像介入方法，该方法由两个模型组成。第一个模型是一种边缘生成模型，用于从损坏的图像中生成完整的边缘信息，第二个模型是用于使用生成的边缘信息和损坏图像的有效内容来修复缺失区域的图像完成模型。图像完成模型中采用了掩模缩小策略来跟踪要修复的区域。在数据集ploce2上对所提出的方法进行定性和定量评估。结果表明我们的方法优于最先进的方法。
In the image inpainting task, the ability to repair both high-frequency and low-frequency information in the missing regions has a substantial influence on the quality of the restored image. However, existing inpainting methods usually fail to consider both high-frequency and low-frequency information simultaneously. To solve this problem, this paper proposes edge information and mask shrinking based image inpainting approach, which consists of two models. The first model is an edge generation model used to generate complete edge information from the damaged image, and the second model is an image completion model used to fix the missing regions with the generated edge information and the valid contents of the damaged image. The mask shrinking strategy is employed in the image completion model to track the areas to be repaired. The proposed approach is evaluated qualitatively and quantitatively on the dataset Places2. The result shows our approach outperforms state-of-the-art methods.
