Web: http://arxiv.org/abs/2205.01741

May 5, 2022, 1:10 a.m. | Lucia Cipolina-Kun, Simone Caenazzo, Gaston Mazzei

cs.CV updates on arXiv.org arxiv.org

Digital art restoration has benefited from inpainting models to correct the
degradation or missing sections of a painting. This work compares three current
state-of-the art models for inpainting of large missing regions. We provide
qualitative and quantitative comparison of the performance by CoModGANs, LaMa
and GLIDE in inpainting of blurry and missing sections of images. We use
Escher's incomplete painting Print Gallery as our test study since it presents
several of the challenges commonly present in restorative inpainting.

art arxiv comparison cv inpainting

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