Next Article in Journal
Improving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks
Next Article in Special Issue
Laser Scanners for High-Quality 3D and IR Imaging in Cultural Heritage Monitoring and Documentation
Previous Article in Journal
Personalized Shares in Visual Cryptography
Previous Article in Special Issue
An Uncertainty-Aware Visual System for Image Pre-Processing
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
J. Imaging 2018, 4(11), 127; https://doi.org/10.3390/jimaging4110127

Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation

Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
*
Author to whom correspondence should be addressed.
Received: 31 August 2018 / Revised: 4 October 2018 / Accepted: 26 October 2018 / Published: 29 October 2018
(This article belongs to the Special Issue Image Enhancement, Modeling and Visualization)
Full-Text   |   PDF [1210 KB, uploaded 29 October 2018]   |  

Abstract

In the image processing pipeline of almost every digital camera, there is a part for removing the influence of illumination on the colors of the image scene. Tuning the parameter values of an illumination estimation method for maximal accuracy requires calibrated images with known ground-truth illumination, but creating them for a given sensor is time-consuming. In this paper, the green stability assumption is proposed that can be used to fine-tune the values of some common illumination estimation methods by using only non-calibrated images. The obtained accuracy is practically the same as when training on calibrated images, but the whole process is much faster since calibration is not required and thus time is saved. The results are presented and discussed. The source code website is provided in Section Experimental Results. View Full-Text
Keywords: chromaticity; color constancy; gray-edge; gray-world; green; illumination estimation; shades-of-gray; standard deviation; unsupervised learning; white balancing chromaticity; color constancy; gray-edge; gray-world; green; illumination estimation; shades-of-gray; standard deviation; unsupervised learning; white balancing
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Banić, N.; Lončarić, S. Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation. J. Imaging 2018, 4, 127.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Imaging EISSN 2313-433X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top