基于生成对抗网络的遮挡图像修复算法

Masked Image Inpainting Algorithm Based on Generative Adversarial Nets

提出一种基于生成对抗网络的遮挡图像修复算法,能够在大量像素缺失的场景下复原出图像的本来面目.该算法不同于其他的样本块搜索复原算法,可直接生成并且填充可能的缺失元素,改进了生成对抗网络生成模型的结构和生成损失的计算方法,具有半监督学习的特点.实验结果表明,在满足图像整体轮廓的前提下,新算法优于其他算法.

A masked image inpainting algorithm based on generative adversarial nets was proposed,which can restore the original image from the lacking of a large number of pixels. Unlike other block search restoration algorithms,the algorithm proposed directly generates possible missing elements and restore them. Due to the improved structure of generated model and the calculation method of generating loss on generative adversarial nets,this article has the characteristics of semi-supervised learning. Experiments show that the proposed method outperforms the existing one on the premise of satisfying the overall contour of the image.