Filling arbitrary missing regions with visually plausible contents is a challenging task. Existing methods have accomplished promising performance for inpainting regular or small defects. However, filling large continuous holes is still difficult due to out of step with the performance requirement for inferring large region contents. Considering that the model needs to predict more missing contents with less information when filling large holes, we first introduce a Residual Gather Module (RGM) for high prediction performance by fully taking advantage of residual learning and capturing feature information from a hierarchical distribution. To obtain both high-quality textures and deep model performance, we propose a novel image translation architecture. Different from U-Net, our skip method is semi-complete form. It eliminates losing details caused by downsampling and is capable of designing very deep generative models. Experiments demonstrate that the proposed method exhibits outstanding performance on filling large missing regions.

用视觉上似是而非的内容填充任意缺失区域是一项具有挑战性的任务。现有方法在修复规则或小缺陷方面取得了有希望的性能。然而，由于与推断大区域内容的性能要求不同步，填充大的连续孔仍然很困难。考虑到模型在填充大孔时需要用更少的信息预测更多的缺失内容，我们首先引入了残差收集模块（RGM），通过充分利用残差学习和从层次分布中捕获特征信息来实现高预测性能。为了获得高质量的纹理和深度模型性能，我们提出了一种新颖的图像翻译架构。与 U-Net 不同，我们的跳过方法是半完全形式。它消除了由下采样导致的细节丢失，并且能够设计非常深的生成模型。实验表明，所提出的方法在填充大缺失区域方面表现出出色的性能。