Paper Info
Title
Inpainting at Modern Camera Resolution by Guided PatchMatch with Auto-curation
Abstract
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 [27] to fill the hole plausibly, establish three guide images consisting of structure, segmentation, depth, and apply a multiply-guided PatchMatch [1] 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 8 $$\,\times \,$$ 8 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 times 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.
Year
DOI
Venue
2022
10.1007/978-3-031-19790-1_4
Computer Vision – ECCV 2022
Keywords
DocType
ISSN
Inpainting, PatchMatch
Conference
0302-9743
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Zhang Lingzhi102.37
Connelly Barnes2172959.07
Kevin Wampler300.34
Sohrab Amirghodsi400.68
Eli Shechtman54340177.94
Zhe Lin63100134.26
Jianbo Shi7102071031.66