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Generative image inpainting with salient prior and relative total variation
Journal of Visual Communication and Image Representation ( IF 2.6 ) Pub Date : 2021-07-16 , DOI: 10.1016/j.jvcir.2021.103231
Hang Shao 1 , Yongxiong Wang 1
Affiliation  

Image inpainting is an important research direction of image processing. The generative adversarial network (GAN), which can reconstruct new reasonable content in the corrupted region, is the most interesting tool in current inpainting technologies. However, the previous deep methods generally need to be pre-added the binary mask representing the corruption location as the extra input. A novel inpainting algorithm which does not require additional external labels is proposed in this paper. The algorithm consists of two parts: corruption recognition module and content inpainting module, which can recognize and fill random corruption. In the recognizer, the salient object from the uncorrupted region is used as the prior for distinguishing corruption. In the inpainting module, a two-stage network is applied to reconstruct the image from coarse content to texture details. To avoid the misdetection in recognition which has a negative impact on the restoration in inpainting, we perform relative total variational filtering on the corrupted image, and use the salient map as the supervision of detail reconstruction. Qualitative and quantitative experiments on multiple datasets verify the effectiveness of our recognition module, the competitive advantage of our inpainting module, and the enlightening significance of our total algorithm in image inpainting.



中文翻译:

具有显着先验和相对总变异的生成图像修复

图像修复是图像处理的一个重要研究方向。生成对抗网络(GAN)可以在损坏的区域重建新的合理内容,是当前修复技术中最有趣的工具。但是,以前的深度方法一般需要预先添加表示损坏位置的二进制掩码作为额外输入。本文提出了一种不需要额外外部标签的新型修复算法。该算法由损坏识别模块和内容修复模块两部分组成,可以识别和填充随机损坏。在识别器中,未损坏区域的显着对象用作区分损坏的先验。在修复模块中,应用两阶段网络将图像从粗略内容重建为纹理细节。为了避免识别中的误检测对修复中的恢复产生负面影响,我们对损坏的图像进行相对全变分滤波,并使用显着图作为细节重建的监督。在多个数据集上的定性和定量实验验证了我们的识别模块的有效性,我们的修复模块的竞争优势,以及我们的整体算法在图像修复中的启发意义。

更新日期:2021-07-22
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