Comparison of the accuracy of different white-balancing options as quantified by their color constancy

2004 ◽  
Author(s):  
J. A. Stephen Viggiano
Keyword(s):  
Color Constancy ◽  
White Balancing

scholarly journals Three-Color Balancing for Color Constancy Correction

2021 ◽  
Vol 7 (10) ◽  
pp. 207
Author(s):  
Teruaki Akazawa ◽  
Yuma Kinoshita ◽  
Sayaka Shiota ◽  
Hitoshi Kiya
Keyword(s):  
Computational Complexity ◽  
Color Constancy ◽  
Ground Truth ◽  
Error Minimization ◽  
Multiple Target ◽  
White Balancing

This paper presents a three-color balance adjustment for color constancy correction. White balancing is a typical adjustment for color constancy in an image, but there are still lighting effects on colors other than white. Cheng et al. proposed multi-color balancing to improve the performance of white balancing by mapping multiple target colors into corresponding ground truth colors. However, there are still three problems that have not been discussed: choosing the number of target colors, selecting target colors, and minimizing error which causes computational complexity to increase. In this paper, we first discuss the number of target colors for multi-color balancing. From our observation, when the number of target colors is greater than or equal to three, the best performance of multi-color balancing in each number of target colors is almost the same regardless of the number of target colors, and it is superior to that of white balancing. Moreover, if the number of target colors is three, multi-color balancing can be performed without any error minimization. Accordingly, we propose three-color balancing. In addition, the combination of three target colors is discussed to achieve color constancy correction. In an experiment, the proposed method not only outperforms white balancing but also has almost the same performance as Cheng’s method with 24 target colors.

A Flying Gray Ball Multi-illuminant Image Dataset for Color Research

2020 ◽  
Vol 64 (5) ◽  
pp. 50411-1-50411-8
Author(s):  
Hoda Aghaei ◽  
Brian Funt
Keyword(s):  
Machine Vision ◽  
Spectral Reflectance ◽  
Color Constancy ◽  
Ground Truth ◽  
Practical Approach ◽  
Post Processing ◽  
Illumination Estimation ◽  
Processing Step ◽  
Image Dataset

Abstract For research in the field of illumination estimation and color constancy, there is a need for ground-truth measurement of the illumination color at many locations within multi-illuminant scenes. A practical approach to obtaining such ground-truth illumination data is presented here. The proposed method involves using a drone to carry a gray ball of known percent surface spectral reflectance throughout a scene while photographing it frequently during the flight using a calibrated camera. The captured images are then post-processed. In the post-processing step, machine vision techniques are used to detect the gray ball within each frame. The camera RGB of light reflected from the gray ball provides a measure of the illumination color at that location. In total, the dataset contains 30 scenes with 100 illumination measurements on average per scene. The dataset is available for download free of charge.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

Contribution of the marginal peripheral retina to color constancy: Evidence obtained due to contact lens with implanted occluder

2019 ◽  
Vol 33 (2) ◽  
pp. 113-123
Author(s):  
G. I. Rozhkova ◽  
E. N. Iomdina ◽  
O. M. Selina ◽  
A. V. Belokopytov ◽  
P. P. Nikolayev
Keyword(s):  
Contact Lens ◽  
Color Constancy ◽  
Peripheral Retina

Color Constancy

2017 ◽  
Author(s):  
Joshua Gert
Keyword(s):  
Visual Representation ◽  
Color Constancy ◽  
Color Appearance ◽  
Fine Grained ◽  
Hybrid View

This chapter presents an account of color constancy that explains a well-known division in the data from color-constancy experiments: So-called “paper matches” exhibit a much higher level of constancy than so-called “hue-saturation matches.” It argues that the visual representation of objective color is the representation of something associated with a function from viewing circumstances to color appearances. Thus, a relatively robust constancy in the representation of objective color is perfectly consistent with a relatively less robust level of constancy in color appearance. The account also endorses Hilbert’s idea that we can represent the color of the illumination on a surface as well as the color of the surface itself. Finally, the chapter addresses an objection to the hybrid view that notes our capacity to make very fine-grained distinctions between the objective colors of surfaces.

Improving Color Constancy by Photometric Edge Weighting

2012 ◽  
Vol 34 (5) ◽  
pp. 918-929 ◽  
Author(s):  
A. Gijsenij ◽  
T. Gevers ◽  
J. van de Weijer
Keyword(s):  
Color Constancy ◽  
Edge Weighting
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