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An efficient and self-adapted approach to the sharpening of color images.

Kau LJ, Lee TL - ScientificWorldJournal (2013)

Bottom Line: With the proposed approach, the discontinuities can be highlighted while most of the original information contained in the image can be retained.Finally, the adjusted channel of Value and that of Hue and Saturation will be integrated to get the sharpened color image.Extensive experiments on natural images will be given in this paper to highlight the effectiveness and efficiency of the proposed approach.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering & Graduate Institute of Computer and Communication Engineering, National Taipei University of Technology, No. 1 Section 3, Chung-Hsiao E. Road, Taipei 10608, Taiwan.

ABSTRACT
An efficient approach to the sharpening of color images is proposed in this paper. For this, the image to be sharpened is first transformed to the HSV color model, and then only the channel of Value will be used for the process of sharpening while the other channels are left unchanged. We then apply a proposed edge detector and low-pass filter to the channel of Value to pick out pixels around boundaries. After that, those pixels detected as around edges or boundaries are adjusted so that the boundary can be sharpened, and those nonedge pixels are kept unaltered. The increment or decrement magnitude that is to be added to those edge pixels is determined in an adaptive manner based on global statistics of the image and local statistics of the pixel to be sharpened. With the proposed approach, the discontinuities can be highlighted while most of the original information contained in the image can be retained. Finally, the adjusted channel of Value and that of Hue and Saturation will be integrated to get the sharpened color image. Extensive experiments on natural images will be given in this paper to highlight the effectiveness and efficiency of the proposed approach.

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Related in: MedlinePlus

The commonly used HSV color model.
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fig1: The commonly used HSV color model.

Mentions: In the proposed algorithm, we use another widely applied color space, the so-called HSV color model instead of the RGB color model, for the sharpening of color images [19]. The HSV color model, which rearranges the geometry of RGB model in a cylindrical coordinate, is shown in Figure 1. As can be seen in Figure 1, the HSV color model which takes the shape of a cone is usually referred to as “hexcone model". In the HSV color model, the component “Hue" is what we normally think of as color. It is usually represented by an angle between 0° and 360°, which indicates the attribute of a visual sensation according to which an area appears to be similar to one of the perceived colors, for example, red, yellow, green, and blue, or to a combination of them. On the other hand, the component “Saturation” is a measure of how different a color appears from a grey of the same lightness. The value of Saturation is usually represented with a value from 0 to 1. When the value is 0, the color is grey, and when the value is 1, the color is a primary color. A faded color is due to a lower saturation level, which means that the color contains more grey. The component “Value” describes the brightness of the color and varies with color saturation. It is usually represented with a value from 0 to 1. When the value is 0, the color will be totally black. With the use of Hue, Saturation, and Value as components, the characteristic of HSV color model is more intuitive and perceptually relevant to human visual system than that of the Cartesian representation of RGB model [19].


An efficient and self-adapted approach to the sharpening of color images.

Kau LJ, Lee TL - ScientificWorldJournal (2013)

The commonly used HSV color model.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3852310&req=5

fig1: The commonly used HSV color model.
Mentions: In the proposed algorithm, we use another widely applied color space, the so-called HSV color model instead of the RGB color model, for the sharpening of color images [19]. The HSV color model, which rearranges the geometry of RGB model in a cylindrical coordinate, is shown in Figure 1. As can be seen in Figure 1, the HSV color model which takes the shape of a cone is usually referred to as “hexcone model". In the HSV color model, the component “Hue" is what we normally think of as color. It is usually represented by an angle between 0° and 360°, which indicates the attribute of a visual sensation according to which an area appears to be similar to one of the perceived colors, for example, red, yellow, green, and blue, or to a combination of them. On the other hand, the component “Saturation” is a measure of how different a color appears from a grey of the same lightness. The value of Saturation is usually represented with a value from 0 to 1. When the value is 0, the color is grey, and when the value is 1, the color is a primary color. A faded color is due to a lower saturation level, which means that the color contains more grey. The component “Value” describes the brightness of the color and varies with color saturation. It is usually represented with a value from 0 to 1. When the value is 0, the color will be totally black. With the use of Hue, Saturation, and Value as components, the characteristic of HSV color model is more intuitive and perceptually relevant to human visual system than that of the Cartesian representation of RGB model [19].

Bottom Line: With the proposed approach, the discontinuities can be highlighted while most of the original information contained in the image can be retained.Finally, the adjusted channel of Value and that of Hue and Saturation will be integrated to get the sharpened color image.Extensive experiments on natural images will be given in this paper to highlight the effectiveness and efficiency of the proposed approach.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering & Graduate Institute of Computer and Communication Engineering, National Taipei University of Technology, No. 1 Section 3, Chung-Hsiao E. Road, Taipei 10608, Taiwan.

ABSTRACT
An efficient approach to the sharpening of color images is proposed in this paper. For this, the image to be sharpened is first transformed to the HSV color model, and then only the channel of Value will be used for the process of sharpening while the other channels are left unchanged. We then apply a proposed edge detector and low-pass filter to the channel of Value to pick out pixels around boundaries. After that, those pixels detected as around edges or boundaries are adjusted so that the boundary can be sharpened, and those nonedge pixels are kept unaltered. The increment or decrement magnitude that is to be added to those edge pixels is determined in an adaptive manner based on global statistics of the image and local statistics of the pixel to be sharpened. With the proposed approach, the discontinuities can be highlighted while most of the original information contained in the image can be retained. Finally, the adjusted channel of Value and that of Hue and Saturation will be integrated to get the sharpened color image. Extensive experiments on natural images will be given in this paper to highlight the effectiveness and efficiency of the proposed approach.

Show MeSH
Related in: MedlinePlus