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S. Bianco, R. Schettini (2021)
Consensus-driven illuminant estimation with GANs, 11605
E. Land (1977)
The retinex theory of color vision.Scientific American, 237 6
Nikola Banić, S. Lončarić (2017)
Unsupervised Learning for Color ConstancyArXiv, abs/1712.00436
O. Ronneberger, P. Fischer, T. Brox (2015)
U-Net: Convolutional Networks for Biomedical Image SegmentationArXiv, abs/1505.04597
Zhihao Li, Zhan Ma (2021)
Robust white balance estimation using joint attention and angular loss optimization, 11605
Hoda Aghaei, B. Funt (2020)
A Flying Gray Ball Multi-illuminant Image Dataset for Color Research
Seoung Oh, Seon Kim (2016)
Approaching the computational color constancy as a classification problem through deep learningArXiv, abs/1608.07951
Yanlin Qian, Sibo Feng, Kang Qian, Miaofeng Wang (2020)
SDE-AWB: a generic solution for 2nd International Illumination Estimation Challenge, 11605
Huanglin Yu, Ke Chen, Kaiqi Wang, Yanlin Qian, Zhaoxiang Zhang, K. Jia (2019)
Cascading Convolutional Color ConstancyArXiv, abs/1912.11180
G. Buchsbaum (1980)
A spatial processor model for object colour perceptionJournal of The Franklin Institute-engineering and Applied Mathematics, 310
Nikola Banić, S. Lončarić (2015)
Color Dog - Guiding the Global Illumination Estimation to Better Accuracy
A Gijsenij, T Gevers (2011)
Color constancy using natural image statistics and scene semantics, 33
Karlo Koščević, Nikola Banić, S. Lončarić (2019)
Color Beaver: Bounding Illumination Estimations for Higher Accuracy
Wu Shi, Chen Loy, Xiaoou Tang (2016)
Deep Specialized Network for Illuminant Estimation
D. Foster (2011)
Color constancyVision Research, 51
G. Finlayson, Roshanak Zakizadeh (2014)
Reproduction Angular Error: An Improved Performance Metric for Illuminant Estimation
Illumination estimation is the essential step of computational color constancy, one of the core parts of various image processing pipelines of modern digital cameras. Having an accurate and reliable illumination estimation is important for reducing the illumination influence on the image colors. To motivate the generation of new ideas and the development of new algorithms in this field, two challenges on illumination estimation were conducted. The main advantage of testing a method on a challenge over testing it on some of the known datasets is the fact that the ground‐truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased. The First illumination estimation challenge (IEC#1) had only a single task, global illumination estimation. The second illumination estimation challenge (IEC#2) was enriched with two additional tracks that encompassed indoor and two‐illuminant illumination estimation. Other main features of it are a new large dataset of images (about 5000) taken with the same camera sensor model, a manual markup accompanying each image, diverse content with scenes taken in numerous countries under a huge variety of illuminations extracted by using the SpyderCube calibration object, and a contest‐like markup for the images from the Cube++ dataset. This article focuses on the description of the past two challenges, algorithms which won in each track, and the conclusions that were drawn based on the results obtained during the first and second challenge that can be useful for similar future developments.
Color Research & Application – Wiley
Published: Aug 1, 2021
Keywords: ; ; ; ; ;
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