DeshadowNet : A Multi-context Embedding Deep Network for Shadow Removal

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationIEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017
PublisherIEEE
Pages2308-2316
ISBN (Electronic)9781538604571
Publication statusPublished - Jul 2017

Publication series

NameConference on Computer Vision and Pattern Recognition (CVPR)
PublisherIEEE
ISSN (Print)1063-6919

Conference

Title30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)
PlaceUnited States
CityHonolulu
Period21 - 26 July 2017

Abstract

Shadow removal is a challenging task as it requires the detection/annotation of shadows as well as semantic understanding of the scene. In this paper, we propose an automatic and end-to-end deep neural network (DeshadowNet) to tackle these problems in a unified manner. DeshadowNet is designed with a multi-context architecture, where the output shadow matte is predicted by embedding information from three different perspectives. The first global network extracts shadow features from a global view. Two levels of features are derived from the global network and transferred to two parallel networks. While one extracts the appearance of the input image, the other one involves semantic understanding for final prediction. These two complementary networks generate multi-context features to obtain the shadow matte with fine local details. To evaluate the performance of the proposed method, we construct the first large scale benchmark with 3088 image pairs. Extensive experiments on two publicly available benchmarks and our large-scale benchmark show that the proposed method performs favorably against several state-of-the-art methods.

Citation Format(s)

DeshadowNet : A Multi-context Embedding Deep Network for Shadow Removal. / Qu, Liangqiong; Tian, Jiandong; He, Shengfeng; Tang, Yandong; Lau, Rynson W. H.

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017. IEEE, 2017. p. 2308-2316 (Conference on Computer Vision and Pattern Recognition (CVPR)).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review