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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.


“Indoor View” [22] (image size: 640 × 428 pixels) processed by just-noticeable difference contrast adjustment (JNDCA). (a) Original image; (b) Original histogram; (c) Histogram Equalization (HE) image; (d) HE histogram; (e) JNDCA image; (f) JNDCA histogram.
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sensors-15-16981-f002: “Indoor View” [22] (image size: 640 × 428 pixels) processed by just-noticeable difference contrast adjustment (JNDCA). (a) Original image; (b) Original histogram; (c) Histogram Equalization (HE) image; (d) HE histogram; (e) JNDCA image; (f) JNDCA histogram.

Mentions: Figure 2a shows an underexposed image containing 119 gray values. The image in Figure 2c is processed by HE and suffers from an excessive enhancement problem: the door, floor, rain shelter, etc., are over-enhanced. Moreover, this image has only 54 gray values because multiple gray values are compressed because of HE. As a result, Figure 2c suffers from the feature loss problem, which causes the textures of the rain shelter to disappear. Figure 2e shows the JNDCA image obtained by applying the JNDCA process. It contains 54 gray values, which is the same as that in Figure 2c. Thus, this image satisfies the minimum discrimination requirement of human visual perception. Through space adjustment between two neighboring gray values, Figure 2e shows improvement in the excessive contrast enhancement problem caused by HE. Figure 2b,d, and f are the histograms of the luminance (Y) component of Figure 2a,c, and e, respectively. The obtained available spaces, “free spaces,” are used in the following processes for further enhancement of image quality.


Visual Contrast Enhancement Algorithm Based on Histogram Equalization.

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

“Indoor View” [22] (image size: 640 × 428 pixels) processed by just-noticeable difference contrast adjustment (JNDCA). (a) Original image; (b) Original histogram; (c) Histogram Equalization (HE) image; (d) HE histogram; (e) JNDCA image; (f) JNDCA histogram.
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Related In: Results  -  Collection

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

sensors-15-16981-f002: “Indoor View” [22] (image size: 640 × 428 pixels) processed by just-noticeable difference contrast adjustment (JNDCA). (a) Original image; (b) Original histogram; (c) Histogram Equalization (HE) image; (d) HE histogram; (e) JNDCA image; (f) JNDCA histogram.
Mentions: Figure 2a shows an underexposed image containing 119 gray values. The image in Figure 2c is processed by HE and suffers from an excessive enhancement problem: the door, floor, rain shelter, etc., are over-enhanced. Moreover, this image has only 54 gray values because multiple gray values are compressed because of HE. As a result, Figure 2c suffers from the feature loss problem, which causes the textures of the rain shelter to disappear. Figure 2e shows the JNDCA image obtained by applying the JNDCA process. It contains 54 gray values, which is the same as that in Figure 2c. Thus, this image satisfies the minimum discrimination requirement of human visual perception. Through space adjustment between two neighboring gray values, Figure 2e shows improvement in the excessive contrast enhancement problem caused by HE. Figure 2b,d, and f are the histograms of the luminance (Y) component of Figure 2a,c, and e, respectively. The obtained available spaces, “free spaces,” are used in the following processes for further enhancement of image quality.

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.