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Color images are essential in computer vision applications such as surveillance and security systems. However, due to the low light conditions, the color of the obtained image deviates from the original color. Moreover, it would produce inaccurate results that limit the performance of these applications. Therefore, this paper proposes a hue preserving uniform illumination image enhancement via triangle similarity criterion in hue–saturation–intensity (HSI) color space. Based on this, the proposed method develops translation and scaling operations to enhance the intensity and saturation. These enhancement processes maintain the hue and features while having minimal effect on the mean brightness. The proposed method and five model-based enhancement methods are evaluated from five perspectives: subjective visual evaluation, the hue and features preservation capabilities, the ability to enhance the contrast and visual information, mean brightness preservation capability, and computational complexity. Results indicate that the proposed method produces better-enhanced images than the other five model-based enhancement methods. [ABSTRACT FROM AUTHOR]

Some color images primarily comprise specific hues, for example, food images predominantly contain a warm hue. Therefore, these hues are essential for creating delicious impressions of food images. This paper proposes a color image enhancement method that can select hues to be enhanced arbitrarily. The current chroma is considered such that near achromatic colors are not over-enhanced. The effectiveness of the proposed method was confirmed through experiments using several food images. [ABSTRACT FROM AUTHOR]

Some color images primarily comprise specific hues, for example, food images predominantly contain a warm hue. Therefore, these hues are essential for creating delicious impressions of food images. This paper proposes a color image enhancement method that can select hues to be enhanced arbitrarily. The current chroma is considered such that near achromatic colors are not over-enhanced. The effectiveness of the proposed method was confirmed through experiments using several food images.

This paper proposes a method for improving saturation in the context of hue-preserving color image enhancement. The proposed method handles colors in an RGB color space, which has the form of a cube, and enhances the contrast of a given image by histogram manipulation, such as histogram equalization and histogram specification, of the intensity image. Then, the color corresponding to a target intensity is determined in a hue-preserving manner, where a gamut problem should be taken into account. We first project any color onto a surface in the RGB color space, which bisects the RGB color cube, to increase the saturation without a gamut problem. Then, we adjust the intensity of the saturation-enhanced color to the target intensity given by the histogram manipulation. The experimental results demonstrate that the proposed method achieves higher saturation than that given by related methods for hue-preserving color image enhancement. [ABSTRACT FROM AUTHOR]

Brightness-robust tracking is always a tough task in the applications of optical signal processing. Conventional red-green-blue (RGB) space based visual tracking always fails due to the sensitiveness to changes in brightness. Hue is a robust color feature existing in the aforementioned unstable condition, but seldom considered by researchers. In this paper, a hue preservation based color space is designed to replace RGB space and to stabilize the tracking accuracy for tracking algorithms in case of severe brightness variation. First, RGB space is mapped to hue-saturation-intensity (HSI) space and the hue component remains constant through a hue preservation principle. Then, the saturation and intensity components which are easily affected by brightness are regularized through shifting and scaling operations. When finally converted back to RGB space, connection lines that connect the three corners of the RGB cube as well as the geometric center of the lines are selected as the new color space. Experimental results demonstrate that when applied to visual tracking algorithms, our color space transformation method can improve the accuracy and stability under the condition of varying brightness, or even in the low-light-level environment. [ABSTRACT FROM AUTHOR]

• We build two complementary terms in a variational framework for enhancing images. • Our model can be performed on each color channel separately while preserving the hue of the input image very satisfactorily. • An efficient numerical implementation of our model is accomplished via the popular split Bregman method. Hue-preserving image enhancement is a very challenging and important research topic in image/video processing and computer vision fields. In the past decades, spectral methods, histogram-based methods, and spatial methods are proposed to partially tackle this problem. However, most of them still suffer from the challenges in enhancing color images while preventing hue shift. In this paper, we build two complementary terms in a variational framework for enhancing images: one is for increasing the image brightness, and the other is for strengthening the edge structure. The two enhancing terms are adaptively united to enhance color images and preserve the original hue simultaneously. The existence and uniqueness of minimizer for the proposed variational model are rigorously established. An efficient numerical implementation of our model is also accomplished via the popular split Bregman method. A number of numerical experiments and comparisons with some state-of-the-art methods are conducted to demonstrate the good performance of the newly proposed model. [ABSTRACT FROM AUTHOR]

Most of the color image enhancement algorithms are implemented in two stages: gray scale image enhancement, which finds the target intensity, and then gamut mapping of the original color coordinates to the target. Therefore, hue preserving gamut mapping is an essential and crucial step, which influences colorfulness. In conventional color mapping methods, color saturation is reduced after intensity modification, which deteriorates subjective image quality. In this paper, a new color enhancement algorithm resulting in high color saturation is proposed. The proposed method employs multiplicative and additive color mapping to improve color saturation without clipping of a color component for increased target intensity as well as decreased cases. This new scheme is fast and effective, therefore, it can be employed to real time applications such as video signal processing. [ABSTRACT FROM AUTHOR]