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Visual Contrast Enhancement Algorithm Based on Histogram Equalization.

Ting CC, Wu BF, Chung ML, Chiu CC, Wu YC - Sensors (Basel) (2015)

Bottom Line: In addition, VCEA reduces the effects of the feature loss problem by using the obtained spaces.Furthermore, VCEA enhances the detailed textures of an image to generate an enhanced image with better visual quality.Experimental results show that images obtained by applying VCEA have higher contrast and are more suited to human visual perception than those processed by HE and other HE-based methods.

View Article: PubMed Central - PubMed

Affiliation: School of Defense Science, Chung Cheng Institute of Technology, National Defense University, Taoyuan 33551, Taiwan. chihchungting@gmail.com.

ABSTRACT
Image enhancement techniques primarily improve the contrast of an image to lend it a better appearance. One of the popular enhancement methods is histogram equalization (HE) because of its simplicity and effectiveness. However, it is rarely applied to consumer electronics products because it can cause excessive contrast enhancement and feature loss problems. These problems make the images processed by HE look unnatural and introduce unwanted artifacts in them. In this study, a visual contrast enhancement algorithm (VCEA) based on HE is proposed. VCEA considers the requirements of the human visual perception in order to address the drawbacks of HE. It effectively solves the excessive contrast enhancement problem by adjusting the spaces between two adjacent gray values of the HE histogram. In addition, VCEA reduces the effects of the feature loss problem by using the obtained spaces. Furthermore, VCEA enhances the detailed textures of an image to generate an enhanced image with better visual quality. Experimental results show that images obtained by applying VCEA have higher contrast and are more suited to human visual perception than those processed by HE and other HE-based methods.

No MeSH data available.


Functional block diagram of visual contrast enhancement algorithm (VCEA).
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sensors-15-16981-f001: Functional block diagram of visual contrast enhancement algorithm (VCEA).

Mentions: VCEA has three major processes: just-noticeable difference contrast adjustment (JNDCA), compressed pixel recovery (CPR), and detailed texture enhancement (DTE). The functional block diagram of VCEA is shown in Figure 1. The details of the three processes of VCEA are as follows:


Visual Contrast Enhancement Algorithm Based on Histogram Equalization.

Ting CC, Wu BF, Chung ML, Chiu CC, Wu YC - Sensors (Basel) (2015)

Functional block diagram of visual contrast enhancement algorithm (VCEA).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541917&req=5

sensors-15-16981-f001: Functional block diagram of visual contrast enhancement algorithm (VCEA).
Mentions: VCEA has three major processes: just-noticeable difference contrast adjustment (JNDCA), compressed pixel recovery (CPR), and detailed texture enhancement (DTE). The functional block diagram of VCEA is shown in Figure 1. The details of the three processes of VCEA are as follows:

Bottom Line: In addition, VCEA reduces the effects of the feature loss problem by using the obtained spaces.Furthermore, VCEA enhances the detailed textures of an image to generate an enhanced image with better visual quality.Experimental results show that images obtained by applying VCEA have higher contrast and are more suited to human visual perception than those processed by HE and other HE-based methods.

View Article: PubMed Central - PubMed

Affiliation: School of Defense Science, Chung Cheng Institute of Technology, National Defense University, Taoyuan 33551, Taiwan. chihchungting@gmail.com.

ABSTRACT
Image enhancement techniques primarily improve the contrast of an image to lend it a better appearance. One of the popular enhancement methods is histogram equalization (HE) because of its simplicity and effectiveness. However, it is rarely applied to consumer electronics products because it can cause excessive contrast enhancement and feature loss problems. These problems make the images processed by HE look unnatural and introduce unwanted artifacts in them. In this study, a visual contrast enhancement algorithm (VCEA) based on HE is proposed. VCEA considers the requirements of the human visual perception in order to address the drawbacks of HE. It effectively solves the excessive contrast enhancement problem by adjusting the spaces between two adjacent gray values of the HE histogram. In addition, VCEA reduces the effects of the feature loss problem by using the obtained spaces. Furthermore, VCEA enhances the detailed textures of an image to generate an enhanced image with better visual quality. Experimental results show that images obtained by applying VCEA have higher contrast and are more suited to human visual perception than those processed by HE and other HE-based methods.

No MeSH data available.