Recent advances in deep learning have shown exciting promise in filling large
holes in natural images with semantically plausible and context aware details,
impacting fundamental image manipulation tasks such as object removal. While
these learning-based methods are significantly more effective in capturing
high-level features than prior techniques, they can only handle very
low-resolution inputs due to memory limitations and difficulty in training.
Even for slightly larger images, the inpainted regions would appear blurry and
unpleasant boundaries become visible. We propose a multi-scale neural patch
synthesis approach based on joint optimization of image content and texture
constraints, which not only preserves contextual structures but also produces
high-frequency details by matching and adapting patches with the most similar
mid-layer feature correlations of a deep classification network. We evaluate
our method on the ImageNet and Paris Streetview datasets and achieved
state-of-the-art inpainting accuracy. We show our approach produces sharper and
more coherent results than prior methods, especially for high-resolution
images.

提出一种基于多尺度神经网络贴片合成方法的图像修复算法，通过联合优化图像内容和纹理约束，不仅保留了上下文结构，而且通过匹配最相似的中间层特征相关性，产生高频细节。在 ImageNet 和 Paris Streetview 数据集上，该方法取得了最先进的修复准确性。