Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute

Computational color constancy (CCC) is to endow computers or cameras with the capability to remove the color bias effect caused by different scene illuminations. The first procedure of CCC is illuminant estimation, i.e., to calculate the illuminant color for a given image scene. Recently, some metho...

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Bibliographic Details
Published in:Applied sciences Vol. 12; no. 3; p. 1175
Main Authors: Luo, Yunhui, Wang, Xingguang, Wang, Qing
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.02.2022
Subjects:
Algorithms
Color
computer color constancy (CCC)
Deep learning
Estimates
illuminant estimation
image enhancement
kernel-based fuzzy c-means clustering (KFCM)
Machine learning
Methods
non-negative least square regression (NLSR)
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:Computational color constancy (CCC) is to endow computers or cameras with the capability to remove the color bias effect caused by different scene illuminations. The first procedure of CCC is illuminant estimation, i.e., to calculate the illuminant color for a given image scene. Recently, some methods directly mapping image features to illuminant estimation provide an effective and robust solution for this issue. Nevertheless, due to diverse image features, it is uncertain to select which features to model illuminant color. In this research, a series of artificial features weaved into a mapping-based illuminant estimation framework is extensively investigated. This framework employs a multi-model structure and integrates the functions of kernel-based fuzzy c-means (KFCM) clustering, non-negative least square regression (NLSR), and fuzzy weighting. By comparing the resulting performance of different features, the features more correlated to illuminant estimation are found in the candidate feature set. Furthermore, the composite features are designed to achieve the outstanding performances of illuminant estimation. Extensive experiments are performed on typical benchmark datasets and the effectiveness of the proposed method has been validated. The proposed method makes illuminant estimation an explicit transformation of suitable image features with regressed and fuzzy weights, which has significant potential for both competing performances and fast implementation against state-of-the-art methods.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app12031175