Image inpainting technology can patch images with missing pixels. Existing methods propose convolutional neural networks to repair corrupted images. The network extracts effective pixels around the missing pixels and uses the encoding–decoding structure to extract valuable information to repair the vacancy. However, if the missing part is too large to provide useful information, the result will be fuzzy, color mixing, and object confusion. In order to patch the large hole image, we propose a new algorithm, the compression–decompression network, based on the research of existing methods. The compression network takes responsibility for inpainting and generating a down-sample image. The decompression network takes responsibility for extending the down-sample image into the original resolution. We use the residual network to construct the compression network and propose a similar pixel selection algorithm to expand the image, which is better than using the super-resolution network. We evaluate our model over Places2 and CelebA data set and use the similarity ratio as the metric. The result shows that our model has better performance when the inpainting task has many conflicts.

图像修复技术可以修补像素缺失的图像。现有方法提出了卷积神经网络来修复损坏的图像。网络提取丢失像素周围的有效像素，并使用编码解码结构提取有价值的信息以修复空缺。但是，如果缺少的部分太大而无法提供有用的信息，则结果将是模糊，混色和对象混淆。为了修补大孔图像，我们在现有方法的基础上提出了一种新的算法，即压缩-减压网络。压缩网络负责修补和生成下采样图像。减压网络负责将下采样图像扩展到原始分辨率。我们使用残差网络构建压缩网络，并提出了一种类似的像素选择算法来扩展图像，这比使用超分辨率网络要好。我们根据Places2和CelebA数据集评估模型，并使用相似率作为指标。结果表明，当修复任务有很多冲突时，我们的模型具有更好的性能。