With well-selected data, homogeneous diffusion inpainting can reconstruct images from sparse data with high quality. While 4K colour images of size 3840 x 2160 can already be inpainted in real time, optimising the known data for applications like image compression remains challenging: Widely used stochastic strategies can take days for a single 4K image. Recently, a first neural approach for this so-called mask optimisation problem offered high speed and good quality for small images. It trains a mask generation network with the help of a neural inpainting surrogate. However, these mask networks can only output masks for the resolution and mask density they were trained for. We solve these problems and enable mask optimisation for high-resolution images through a neuroexplicit coarse-to-fine strategy. Additionally, we improve the training and interpretability of mask networks by including a numerical inpainting solver directly into the network. This allows to generate masks for 4K images in around 0.6 seconds while exceeding the quality of stochastic methods on practically relevant densities. Compared to popular existing approaches, this is an acceleration of up to four orders of magnitude.

翻译：通过精心选择的数据，均匀扩散修复可从稀疏数据中高质量地重建图像。尽管3840×2160大小的4K彩色图像已能实现实时修复，但为图像压缩等应用优化已知数据仍具挑战：广泛使用的随机策略处理单张4K图像可能需要数天时间。近期，针对这一所谓掩膜优化问题的首个神经方法在小型图像上实现了高速与良好质量，该方法借助神经修复替代模型训练掩膜生成网络。然而，这类掩膜网络仅能输出其训练时对应的分辨率和掩膜密度。我们通过一种神经显式粗到细策略解决了这些问题，并实现了高分辨率图像的掩膜优化。此外，通过将数值修复求解器直接集成到网络中，我们改进了掩膜网络的训练与可解释性。这使得我们能在约0.6秒内生成4K图像的掩膜，同时在实际相关密度上超越随机方法的质量。与现有流行方法相比，这实现了高达四个数量级的加速。