Deep Dichromatic Model Estimation under AC Light Sources

Jun Sang Yoo, Chan Ho Lee, Jong Ok Kim

Research output: Contribution to journalArticlepeer-review

Abstract

The dichromatic reflection model has been popularly exploited for computer vison tasks, such as color constancy and highlight removal. However, dichromatic model estimation is an severely ill-posed problem. Thus, several assumptions have been commonly made to estimate the dichromatic model, such as white-light (highlight removal) and the existence of highlight regions (color constancy). In this paper, we propose a spatio-temporal deep network to estimate the dichromatic parameters under AC light sources. The minute illumination variations can be captured with high-speed camera. The proposed network is composed of two sub-network branches. From high-speed video frames, each branch generates chromaticity and coefficient matrices, which correspond to the dichromatic image model. These two separate branches are jointly learned by spatio-temporal regularization. As far as we know, this is the first work that aims to estimate all dichromatic parameters in computer vision. To validate the model estimation accuracy, it is applied to color constancy and highlight removal. Both experimental results show that the dichromatic model can be estimated accurately via the proposed deep network.

Original languageEnglish
Article number9508197
Pages (from-to)7064-7073
Number of pages10
JournalIEEE Transactions on Image Processing
Volume30
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • AC light
  • Dichromatic reflection model
  • color constancy
  • highlight removal

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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