Gradient-guided color image contrast and saturation enhancement. (9th June 2017)

• Record Type: Journal Article
• Authors: Shi, Haiyan; Kwok, Ngaiming; Fang, Gu; Lin, Stephen Ching-Feng; Lee, Ann; Li, Huaizhong; Yu, Ying-Hao
• Is Part Of: International journal of advanced robotic systems. Volume 14:Number 3(2017:May/Jun.)
• Publication Date: 2017-06-09
• Subjects: Contrast enhancement -- saturation enhancement -- conversion free -- RGB manipulation; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881417711683
• Languages: English
• ISSNs: 1729-8806

Abstract

Digital color images are capable of presenting hue, saturation, and brightness perceptions. Therefore, quality improvement of color images should be taken into account to enhance all three stimuli. An effective method is proposed that aims at enriching the colorfulness, vividness, and contrast of color images simultaneously. In this method, color correction based on magnitude stretching is carried out first, image enhancement is then derived from an intensity-guided operation that concurrently improves the contrast and saturation qualities. Furthermore, the proposed methodology mitigates the heavy computational burden arising from the need to transform the source color space into an alternative color space in conventional approaches. Experiments had been conducted using a collection of real-world images captured under various environmental conditions. Image quality improvements were observed both from subjective viewing and quantitative evaluation metrics in colorfulness, saturation, and contrast.