Comparison of CoModGans, LaMa and GLIDE for Art Inpainting: Completing M.C Escher's Print Gallery

Lucia Cipolina-Kun, Gaston Mazzei, Simone Caenazzo

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)

Abstract

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 of CoModGans, LaMa and GLIDE in inpainting 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.
Original languageEnglish
Title of host publicationProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
PublisherComputer Vision Foundation
Pages716-724
Number of pages8
DOIs
Publication statusPublished - 1 Jun 2022
EventComputer Vision and Pattern Recognition (CVPR) - New Orleans, United States
Duration: 19 Jun 202225 Jun 2022

Conference

ConferenceComputer Vision and Pattern Recognition (CVPR)
Country/TerritoryUnited States
Period19/06/2225/06/22

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