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 image-enhancement methods cannot be applied to state-of-the-art enhancement methods such as deep-learning-based 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-learning-based enhancement methods. Furthermore, it allows us not only to correct hue but also to maintain the performance of image-enhancement methods.
Highlights
Single-image enhancement is one of the most typical image processing techniques
In this paper, we proposed a novel hue-correction scheme based on the constant-hue plane in the RGB color space for color-image enhancement
To derive the proposed scheme, we generalized a hue-preserving method based on the constant-hue plane
Summary
Single-image enhancement is one of the most typical image processing techniques. The purpose of enhancing images is to show hidden details in unclear low-quality images. In addition to image enhancement, hue-preserving image-processing methods have been studied in other research areas such as image denoising [32], but they are designed for specific algorithms For this reason, we propose a novel huepreserving image-enhancement scheme that is applicable to any existing image-enhancement method including deeplearning-based ones. The use of a specific algorithm for enhancement prevents us from applying hue-preserving enhancement to the state-of-the-art enhancement algorithms For this reason, we discuss the first attempt to develop a hue-correction scheme that is applicable to any image-enhancement method including deep-learning-based ones [see Fig. 1(b)]. We first enhance an input image I by using a state-of-the-art image-enhancement method such as Retinex- and deep-learning-based ones This enhancement will cause hue distortion in the enhanced image I. Hue correction enables us to match hue components before and after the enhancement and to maintain the performance of image enhancement
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