Bright Chromagenic Algorithm for Illuminant Estimation

Fredembach, Clément; Finlayson, Graham D.

doi:10.2352/j.imagingsci.technol.(2008)52:4(040906)

Cited by 28 publications

(49 citation statements)

References 30 publications

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“…We use an illuminant estimation method adapted from chromagenic estimation methods originally proposed by Finlayson et al [13] and a modified method proposed by Fredembach and Finlayson [14], for consistent and improved performance [23]. Moreover, these methods use two images of a scene, which is in line with our proposed spectrogenic imaging system.…”

Section: Illuminant Estimationmentioning

confidence: 90%

“…For this, the system must be calibrated under the same environment (illuminant) under which the image is captured. We propose to estimate this illuminant from the two images of a scene, one normal RGB image (RGB) and one filtered RGB image (RGB F ), using the chromagenic algorithm from Finlayson et al [13] or the bright-chromagenic algorithm from Fredembach and Finlayson [14]. The filtered RGB image is the RGB image obtained by filtering through a special optical filter.…”

Section: Proposed Spectrogenic Imaging System and Methodologymentioning

confidence: 99%

“…There are many different illuminant estimation methods proposed in the literature, such as grayworld [17], max-RGB [18], gamut based algorithm [19], neural networks based [20], color-bycorrelation [21], Bayesian based [22] and chromagenic color constancy [13,14]. We use an illuminant estimation method adapted from chromagenic estimation methods originally proposed by Finlayson et al [13] and a modified method proposed by Fredembach and Finlayson [14], for consistent and improved performance [23].…”

Section: Illuminant Estimationmentioning

confidence: 99%

“…Fredembach and Finlayson [14] proposed the bright-chromagenic algorithm which uses a certain percentage of the brightest pixels (typically 1-3%) in an image, rather than all the pixels in the original chromagenic algorithm [13]. The brightest pixels are defined as the ones with the largest C 2 R +C 2 G +C 2 B value.…”

Section: Illuminant Estimationmentioning

confidence: 99%

“…Two images of a scene are taken simultaneously: a normal RGB, and a filtered RGB using a special optical filter. The illuminant under which these images are captured is then estimated using a chromagenic based illuminant estimation method [13,14]. The combination of the two images gives a 6-band multispectral image.…”

Section: Introductionmentioning

confidence: 99%

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Spectrogenic imaging: A novel approach to multispectral imaging in an uncontrolled environment

Shrestha¹,

Hardeberg²

2014

Opt. Express

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Abstract:Increasing the number of imaging channels beyond the conventional three has been shown to be beneficial for a wide range of applications. However, it is mostly limited to imaging in a controlled environment, where the capture environment (illuminant) is known a priori. We propose here a novel system and methodology for multispectral imaging in an uncontrolled environment. Two images of a scene, a normal RGB and a filtered RGB are captured. The illuminant under which an image is captured is estimated using a chromagenic based algorithm, and the multispectral system is calibrated automatically using the estimated illuminant. A 6-band multispectral image of a scene is obtained from the two RGB images. The spectral reflectances of the scene are then estimated using an appropriate spectral estimation method. The proposed concept and methodology is generic one, as it is valid in whatever way we acquire the two images of a scene. A system that can acquire two images of a scene can be realized, for instance in two shots using a digital camera and a filter, or in a single shot using a stereo camera, or a custom color filter array design. Simulation experiments using a stereo camera based system confirms the effectiveness of the proposed method. This could be useful in many imaging applications and computer vision.

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Section: Illuminant Estimationmentioning

confidence: 90%

Section: Proposed Spectrogenic Imaging System and Methodologymentioning

confidence: 99%

Section: Illuminant Estimationmentioning

confidence: 99%

Section: Illuminant Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Spectrogenic imaging: A novel approach to multispectral imaging in an uncontrolled environment

Shrestha¹,

Hardeberg²

2014

Opt. Express

View full text Add to dashboard Cite

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Analysis of biases in automatic white balance datasets and methods

Buzzelli

Zini

Bianco

et al. 2022

Color Research & Application

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Annotated datasets for automatic white balance (AWB) are used for the evaluation and, when necessary, the training, of AWB methods. Relying on such datasets requires awareness of the potential bias in their content and characteristics: some methods are designed to rely on the presence of particular elements, such as human skin, while other methods learn implicit relationships between image content and light properties from training data. The dependency on these relationships makes it fundamental to understand whether the available datasets are actually representative of common application scenarios, such as the presence of human subjects, the diversity of composition, or the illumination conditions. In this paper we overview the most common datasets for Automatic White Balance, including those for single as well as multiple illuminant estimation, providing a critical analysis on their characteristics. Furthermore, we identify a number of existing methods for single illuminant estimation, as a representative pool of approaches to the problem with various levels of complexity. We investigate how the performance of these correlate to the image content of common datasets.

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