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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.


Comparison results for the image “Landscape” [23] (image size: 596 × 397 pixels). (a) Original image; (b) Histogram Equalization (HE); (c) Bi-histogram equalization (BBHE); (d) Recursive mean-separate histogram equalization (RMSHE) (r = 2); (e) Dualistic sub-image histogram equalization (DSIHE); (f) Recursive sub-image histogram equalization (RSIHE) (r = 2); (g) Bi-histogram equalization with a plateau level (BHEPL); (h) Dynamic quadrants histogram equalization plateau limit (DQHEPL); (i) Visual contrast enhancement algorithm (VCEA).
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sensors-15-16981-f006: Comparison results for the image “Landscape” [23] (image size: 596 × 397 pixels). (a) Original image; (b) Histogram Equalization (HE); (c) Bi-histogram equalization (BBHE); (d) Recursive mean-separate histogram equalization (RMSHE) (r = 2); (e) Dualistic sub-image histogram equalization (DSIHE); (f) Recursive sub-image histogram equalization (RSIHE) (r = 2); (g) Bi-histogram equalization with a plateau level (BHEPL); (h) Dynamic quadrants histogram equalization plateau limit (DQHEPL); (i) Visual contrast enhancement algorithm (VCEA).

Mentions: Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9 show experimental results for VCEA in comparison with those for HE [1] and other HE-based methods: brightness-preserving bi-histogram equalization (BBHE) [2], recursive mean-separate histogram equalization (RMSHE) [11], equal area dualistic sub-image histogram equalization (DSIHE) [10], recursive sub-image histogram equalization (RSIHE) [14], bi-histogram equalization with a plateau level (BHEPL) [7], and dynamic quadrants histogram equalization plateau limit (DQHEPL) [17].


Visual Contrast Enhancement Algorithm Based on Histogram Equalization.

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

Comparison results for the image “Landscape” [23] (image size: 596 × 397 pixels). (a) Original image; (b) Histogram Equalization (HE); (c) Bi-histogram equalization (BBHE); (d) Recursive mean-separate histogram equalization (RMSHE) (r = 2); (e) Dualistic sub-image histogram equalization (DSIHE); (f) Recursive sub-image histogram equalization (RSIHE) (r = 2); (g) Bi-histogram equalization with a plateau level (BHEPL); (h) Dynamic quadrants histogram equalization plateau limit (DQHEPL); (i) Visual contrast enhancement algorithm (VCEA).
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16981-f006: Comparison results for the image “Landscape” [23] (image size: 596 × 397 pixels). (a) Original image; (b) Histogram Equalization (HE); (c) Bi-histogram equalization (BBHE); (d) Recursive mean-separate histogram equalization (RMSHE) (r = 2); (e) Dualistic sub-image histogram equalization (DSIHE); (f) Recursive sub-image histogram equalization (RSIHE) (r = 2); (g) Bi-histogram equalization with a plateau level (BHEPL); (h) Dynamic quadrants histogram equalization plateau limit (DQHEPL); (i) Visual contrast enhancement algorithm (VCEA).
Mentions: Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9 show experimental results for VCEA in comparison with those for HE [1] and other HE-based methods: brightness-preserving bi-histogram equalization (BBHE) [2], recursive mean-separate histogram equalization (RMSHE) [11], equal area dualistic sub-image histogram equalization (DSIHE) [10], recursive sub-image histogram equalization (RSIHE) [14], bi-histogram equalization with a plateau level (BHEPL) [7], and dynamic quadrants histogram equalization plateau limit (DQHEPL) [17].

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.