APSIPA Transactions on Signal and Information Processing > Vol 9 > Issue 1

Hue-correction scheme considering CIEDE2000 for color-image enhancement including deep-learning-based algorithms

Yuma Kinoshita, Tokyo Metropolitan University, Japan, ykinoshita@tmu.ac.jp , Hitoshi Kiya, Tokyo Metropolitan University, Japan
 
Suggested Citation
Yuma Kinoshita and Hitoshi Kiya (2020), "Hue-correction scheme considering CIEDE2000 for color-image enhancement including deep-learning-based algorithms", APSIPA Transactions on Signal and Information Processing: Vol. 9: No. 1, e19. http://dx.doi.org/10.1017/ATSIP.2020.17

Publication Date: 10 Sep 2020
© 2020 Yuma Kinoshita and Hitoshi Kiya
 
Subjects
 
Keywords
Color image enhancementColor correctionCIELAB color spaceDeep learningHue-preserving image enhancement
 

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This is published under the terms of the Creative Commons Attribution licence.

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In this article:
I. INTRODUCTION 
II. RELATED WORK 
III. COLOR SPACES 
IV. PROPOSED HUE-CORRECTION SCHEME 
V. SIMULATION 
VI. CONCLUSION 

Abstract

In this paper, we propose a novel hue-correction scheme for color-image-enhancement algorithms including deep-learning-based ones. Although hue-correction schemes for color-image enhancement have already been proposed, there are no schemes that can both perfectly remove perceptual hue-distortion on the basis of CIEDE2000 and be applicable to any image-enhancement algorithms. In contrast, the proposed scheme can perfectly remove hue distortion caused by any image-enhancement algorithm such as deep-learning-based ones on the basis of CIEDE2000. Furthermore, the use of a gamut-mapping method in the proposed scheme enables us to compress a color gamut into an output RGB color gamut, without hue changes. Experimental results show that the proposed scheme can completely correct hue distortion caused by image-enhancement algorithms while maintaining the performance of the algorithms and ensuring the color gamut of output images.

DOI:10.1017/ATSIP.2020.17