Enhancement of Low Illumination Images based on an Optimal Hyperbolic Tangent Profile. (August 2018)

• Record Type: Journal Article
• Authors: Chi Liu, San; Liu, Shilong; Wu, Hongkun; Arifur Rahman, Md; Ching-Feng Lin, Stephen; Yeow Wong, Chin; Kwok, Ngaiming; Shi, Haiyan
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 538
• Page End: 550
• Publication Date: 2018-08
• Subjects: Contrast enhancement -- Low illumination images -- Optimum hyperbolic tangent mapping -- Hybrid linear weighting; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.08.026
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Contrast enhancement is a critical pre-processing stage for many image based applications. It is frequently encountered that the illumination condition, while capturing the image, is imperfect. Specific algorithms have to be applied to restore these images from, for instance, the degradation due to low illumination. An adaptive enhancement method is developed here that tackles the image quality enhancement problem from an optimization perspective. In particular, the input image intensity is mapped to the output based on a weighted hybrid of a hyperbolic tangent and a linear profile. The mapping parameters are optimized, with regard to maximizing the image global entropy, by using the Golden Section Search algorithm for its implementation efficiency. Moreover, user interventions are not necessary. Better qualitative and comparable quantitative performances are obtained from experiments, with regard to the increase of brightness, information content and suppression of unwanted artifacts, as compared to recent profile mapping based methods.