  Recently, deep models have established SOTA performance for low-resolution
image inpainting, but they lack fidelity at resolutions associated with modern
cameras such as 4K or more, and for large holes. We contribute an inpainting
benchmark dataset of photos at 4K and above representative of modern sensors.
We demonstrate a novel framework that combines deep learning and traditional
methods. We use an existing deep inpainting model LaMa to fill the hole
plausibly, establish three guide images consisting of structure, segmentation,
depth, and apply a multiply-guided PatchMatch to produce eight candidate
upsampled inpainted images. Next, we feed all candidate inpaintings through a
novel curation module that chooses a good inpainting by column summation on an
8x8 antisymmetric pairwise preference matrix. Our framework's results are
overwhelmingly preferred by users over 8 strong baselines, with improvements of
quantitative metrics up to 7.4 over the best baseline LaMa, and our technique
when paired with 4 different SOTA inpainting backbones improves each such that
ours is overwhelmingly preferred by users over a strong super-res baseline.
