Intrinsic image decomposition: A comprehensive review

Yupeng Ma, Xiaoyi Feng, Xiaoyue Jiang, Zhaoqiang Xia, Jinye Peng

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

15 Scopus citations

Abstract

Image understanding and analysis is one of the important tasks in the image processing. Multiple factors influence the appearance of an object in an image. However, extracting the intrinsic images from the observer image can eliminate the environmental impact effectively and make the image understanding more accurately. The intrinsic images represent the inherent shape, color and texture information of the object. Intrinsic image decomposition is recovering shading image and reflectance image from a single input image and remains a challenging problem because of its severely ill-posed problem. In order to deal with these problems, researches have proposed various algorithms for decomposing the intrinsic image. In this paper we survey the recent advances in intrinsic image decomposition. First, we introduce the existing datasets for intrinsic image decomposition. Second, we introduce and analyze the existing intrinsic image decomposition algorithms. Finally, we use the existing algorithms to experiment on the intrinsic image datasets, and analyze and summarize the experimental results.

Original languageEnglish
Title of host publicationImage and Graphics - 9th International Conference, ICIG 2017, Revised Selected Papers
EditorsYao Zhao, David Taubman, Xiangwei Kong
PublisherSpringer Verlag
Pages626-638
Number of pages13
ISBN (Print)9783319716060
DOIs
StatePublished - 2017
Event9th International Conference on Image and Graphics, ICIG 2017 - Shanghai, China
Duration: 13 Sep 201715 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10666 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Image and Graphics, ICIG 2017
Country/TerritoryChina
CityShanghai
Period13/09/1715/09/17

Keywords

  • Computer vision
  • Intrinsic image dataset
  • Intrinsic image decomposition
  • Retinex theory

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