Effective cross-sensor color constancy using a dual-mapping strategy

Shuwei Yue, Minchen Wei

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Deep neural networks (DNNs) have been widely used for illuminant estimation, which commonly requires great efforts to collect sensor-specific data. In this paper, we propose a dual-mapping strategy—the DMCC method. It only requires the white points captured by the training and testing sensors under a D65 condition to reconstruct the image and illuminant data, and then maps the reconstructed image into sparse features. These features, together with the reconstructed illuminants, were used to train a lightweight multi-layer perceptron (MLP) model, which can be directly used to estimate the illuminant for the testing sensor. The proposed model was found to have performance comparable to other state-of-the-art methods, based on the three available datasets. Moreover, the smaller number of parameters, faster speed, and not requiring data collection using the testing sensor make it ready for practical deployment. This paper is an extension of Yue and Wei [Color and Imaging Conference (2023)], with more detailed results, analyses, and discussions.

Original languageEnglish
Pages (from-to)329-337
Number of pages9
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Volume41
Issue number2
DOIs
Publication statusPublished - 1 Feb 2024

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition

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