Research

User-assisted image shadow removal


Reference:

Gong, H. and Cosker, D., 2017. User-assisted image shadow removal. Image and Vision Computing, 62, pp. 19-27.

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Official URL:

http://dx.doi.org/10.1016/j.imavis.2017.04.001

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Abstract

This paper presents a novel user-aided method for texture-preserving shadow removal from single images requiring simple user input. Compared with the state-of-the-art, our algorithm offers the most flexible user interaction to date and produces more accurate and robust shadow removal under thorough quantitative evaluation. Shadow masks are first detected by analysing user specified shadow feature strokes. Sample intensity profiles with variable interval and length around the shadow boundary are detected next, which avoids artefacts raised from uneven boundaries. Texture noise in samples is then removed by applying local group bilateral filtering, and initial sparse shadow scales are estimated by fitting a piecewise curve to intensity samples. The remaining errors in estimated sparse scales are removed by local group smoothing. To relight the image, a dense scale field is produced by in-painting the sparse scales. Finally, a gradual colour correction is applied to remove artefacts due to image post-processing. Using state-of-the-art evaluation data, we quantitatively and qualitatively demonstrate our method to outperform current leading shadow removal methods.

Details

Item Type Articles
CreatorsGong, H.and Cosker, D.
DOI10.1016/j.imavis.2017.04.001
Related URLs
URLURL Type
http://www.scopus.com/inward/record.url?scp=85018877931&partnerID=8YFLogxKUNSPECIFIED
Uncontrolled Keywordscolour correction,curve fitting,image shadow removal,smoothing,user-assisted computer vision,electrical and electronic engineering
DepartmentsFaculty of Science > Computer Science
Research CentresEPSRC Centre for Doctoral Training in Statistical Mathematics (SAMBa)
RefereedYes
StatusPublished
ID Code55999

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