Comparison of CoModGANs, LaMa and GLIDE for Art Inpainting- Completing M.C Escher's Print Gallery
ML Collective（ML集体）；University of Bristol（布里斯托大学）；Riskcare Ltd.（Riskcare有限公司）；University Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；LISN（法国国家信息与自动化研究所跨学科数字科学实验室）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
