Hue-Correction Scheme Based on Constant-Hue Plane for Deep-Learning-Based Color-Image Enhancement

Abstract

In this paper, we propose a novel hue-correction scheme based on the constant-hue plane in the RGB color space for deep-learning-based color-image enhancement. Existing hue-preserving imageenhancement methods cannot be applied to state-of-the-art enhancement methods such as deep-learningbased ones. Our main contributions are a discussion on the enhancement performance and the hue distortion of existing image-enhancement methods as well as the first attempt to make hue correction applicable to any existing image-enhancement methods including deep-learning-based ones. This novel scheme is carried out on the basis of the constant-hue plane in the RGB color space. In simulations, we first evaluated conventional image-enhancement methods in terms of the enhancement performance and hue distortion by using five objective metrics: the maximally saturated color similarity, the hue difference in CIEDE2000, discrete entropy, HIGRADE, and NIQMC. The experimental results show that recent deep-learning-based methods have a higher enhancement performance but cause images to be hue-distorted. In addition, the proposed scheme is demonstrated to be effective for suppressing hue distortion even under the use of deep-learningbased enhancement methods. Furthermore, it allows us not only to correct hue but also to maintain the performance of image-enhancement methods.

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

“…From equations (19) and (20), equation (23) results in the problem asking for m [see Fig. 1(c)] as follows.…”

“…Naik and Murthy showed conditions for preserving hue defined in the HSI color space without color gamut problems [20]. On the basis of Naik's work, several huepreserving methods have already been proposed [21][22][23]. In particular, Kinoshita and Kiya made it possible to remove hue distortion caused by any image-enhancement algorithm by replacing the maximally saturated colors on the constant-hue planes of an enhanced image with those of an input image [23].…”

“…Kinoshita's method [23] cannot remove perceptual hue-distortion, although it can be applied to any image-enhancement algorithm.Azetsu's method [24] cannot be applied to state-of-the-art image-enhancement algorithms such as deep-learning-based ones, although it can remove perceptual hue-distortion.…”

“…To avoid color distortion, hue-preserving imageenhancement methods have also been studied [20][21][22][23][24]. These hue-preserving methods aim not only to enhance images, but also to preserve hue components of input images.…”

“…These hue-preserving methods aim not only to enhance images, but also to preserve hue components of input images. In particular, by extending Naik's work, Kinoshita and Kiya made it possible to remove hue distortion caused by any image-enhancement algorithm by replacing the maximally saturated colors on the constant-hue planes of an enhanced image with those of an input image [23].…”