Physics Based Image Deshadowing Using Local Linear Model

Tamir Einy, Efrat Immer, Gilad Vered, Shai Avidan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Image deshadowing algorithms remove shadows from images. This requires both detecting where the shadow is and, once detected, removing it from the image. This work focuses on the shadow removal part. We follow a common physical shadow formation model and learn its parameters using a deep neural network. Our model consists of an existing network for shadow detection, and a novel network for shadow removal. The shadow removal network gets the predicted mask of the shadow region and the shadow image and predicts six parameters per pixel. Remarkably, a straightforward network architecture, that is considerably smaller compared to alternative methods, produces better results on standard datasets1.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
PublisherIEEE Computer Society
Pages3011-3019
Number of pages9
ISBN (Electronic)9781665487399
DOIs
StatePublished - 2022
Event2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 - New Orleans, United States
Duration: 19 Jun 202220 Jun 2022

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2022-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
Country/TerritoryUnited States
CityNew Orleans
Period19/06/2220/06/22

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