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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” processed by compressed pixel recovery (CPR). (a) CPR image; (b) CPR histogram.
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sensors-15-16981-f003: “Indoor View” processed by compressed pixel recovery (CPR). (a) CPR image; (b) CPR histogram.

Mentions: Figure 3a is obtained by applying the CPR process. Through this process, many compressed gray values such as the textures of the rain shelter are recovered. Figure 3a contains 119 gray values, which is the same number as that in the original image. The CPR process effectively mitigates the feature loss problem caused by HE. It also makes Figure 3a appear better than the JNDCA image because the lost features are recovered in the CPR image. Figure 3b is the histogram of the luminance (Y) component of Figure 3a. Because of the recovery of the compressed gray values, the number of gray values in Figure 3b is more than that in Figure 2f.


Visual Contrast Enhancement Algorithm Based on Histogram Equalization.

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

“Indoor View” processed by compressed pixel recovery (CPR). (a) CPR image; (b) CPR histogram.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16981-f003: “Indoor View” processed by compressed pixel recovery (CPR). (a) CPR image; (b) CPR histogram.
Mentions: Figure 3a is obtained by applying the CPR process. Through this process, many compressed gray values such as the textures of the rain shelter are recovered. Figure 3a contains 119 gray values, which is the same number as that in the original image. The CPR process effectively mitigates the feature loss problem caused by HE. It also makes Figure 3a appear better than the JNDCA image because the lost features are recovered in the CPR image. Figure 3b is the histogram of the luminance (Y) component of Figure 3a. Because of the recovery of the compressed gray values, the number of gray values in Figure 3b is more than that in Figure 2f.

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.