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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

θeth = 16 for HVD. (a) The image “Moon surface” (256 × 256 grey image). (b) Edges detected by HVD with LPF. (c and d) Sharpened results by HVD with scaling factors 0.5 and 1.0, respectively.
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fig15: θeth = 16 for HVD. (a) The image “Moon surface” (256 × 256 grey image). (b) Edges detected by HVD with LPF. (c and d) Sharpened results by HVD with scaling factors 0.5 and 1.0, respectively.

Mentions: In addition to the first nine color test images in Table 1, we also investigate the usefulness of the proposed approach on the three grey-scale test image, that is, the image “Neck”, the image “Moon surface”, and the image “Goldhill”. We first look at the test image “Neck”, a medical image, in Figure 14. As can be seen in Figures 14(c) and 14(d), the contour of the cervical vertebra has become quite obvious after the sharpening process when compared with that of the original image in Figure 14(a). The sharpened results for the test image “Moon surface” are shown in Figures 15(c) and 15(d), respectively. As can be seen in Figures 15(c) and 15(d), those cavities and mounds have become more conspicuous when compared with the original image in Figure 15(a). Finally, for the test image “Goldhill” in Figure 16, the contour of the roof tiles, and the outline of the windows have become more distinct (Figures 16(c) and 16(d)) than that of in the original image (Figure 16(a)).


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

Kau LJ, Lee TL - ScientificWorldJournal (2013)

θeth = 16 for HVD. (a) The image “Moon surface” (256 × 256 grey image). (b) Edges detected by HVD with LPF. (c and d) Sharpened results by HVD with scaling factors 0.5 and 1.0, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig15: θeth = 16 for HVD. (a) The image “Moon surface” (256 × 256 grey image). (b) Edges detected by HVD with LPF. (c and d) Sharpened results by HVD with scaling factors 0.5 and 1.0, respectively.
Mentions: In addition to the first nine color test images in Table 1, we also investigate the usefulness of the proposed approach on the three grey-scale test image, that is, the image “Neck”, the image “Moon surface”, and the image “Goldhill”. We first look at the test image “Neck”, a medical image, in Figure 14. As can be seen in Figures 14(c) and 14(d), the contour of the cervical vertebra has become quite obvious after the sharpening process when compared with that of the original image in Figure 14(a). The sharpened results for the test image “Moon surface” are shown in Figures 15(c) and 15(d), respectively. As can be seen in Figures 15(c) and 15(d), those cavities and mounds have become more conspicuous when compared with the original image in Figure 15(a). Finally, for the test image “Goldhill” in Figure 16, the contour of the roof tiles, and the outline of the windows have become more distinct (Figures 16(c) and 16(d)) than that of in the original image (Figure 16(a)).

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