Algorithms of halftoning color images with edge enhancement

Lai, Jim Z. C.; Chen, Chia-Chi

doi:10.1016/s1047-3203(03)00041-5

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…The performance of the proposed algorithm and four other halftoning algorithms were compared. Figures 13(a)-13(e) show the scanned results of Floyd and Steinberg's error diffusion, Jarvis' error diffusion, 4 Eschbach and Knox's edgeenhanced error diffusion, 7 Lai and Chen's error diffusion using different error filters, 8 and the proposed algorithm.…”

Section: A Comparison Of the Halftone Resultsmentioning

confidence: 99%

“…From Figs. 13(b) and 13(d), a large filter size was found to sharpen the image more, even when different error filters (Jarvis and Stucki filter4,5 ) were applied to the YCbCr space, as in Fig.13(d) 8. In Fig.13(e), dot-elimination artifacts were more effectively removed by the inclusion of the error scaling factor below the text lines, which generated a homogeneous dot pattern below the text lines and increased the quality of the resulting image.…”

mentioning

confidence: 95%

“…7 While a smaller value for the global parameter causes blurring with respect to the original grayscale image, large values cause sharpness. More recently, Lai and Chen proposed an edge-enhanced algorithm for color images, 8 where the RGB coordinate system is transformed into another coordinate system, for example YIQ or YCbCr, making the color components more independent in the new coordinate system. The error diffusion algorithm is then applied using different filters for each component of YCbCr.…”

Section: Introductionmentioning

confidence: 99%

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Text-Enhanced Error Diffusion Using Multiplicative Parameters and Error Scaling Factor

Jae-Hyun¹,

Son²,

Cho³

et al. 2006

jist

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Text-enhanced error diffusion is proposed to sharpen text regions in complex documents, including natural images with a multifunctional printer. To enhance the sharpness of the text regions, the input image is segmented into text and background regions using the maximum gradient difference. Edge-enhanced error diffusion is then applied to the text regions to sharpen the text, while Floyd and Steinberg's error diffusion is applied to the background regions to obtain smooth dot patterns. However, this combination of algorithms can generate two kinds of artifact around the text regions: boundary and dot-elimination artifacts. Boundary artifacts are a series of dots distributed around text blocks, and this propagation error generated below a text line by the edge-enhanced error diffusion is largely diffused forward into the background region. Thus, to gradually decrease these propagation errors, a grayscale dilation operator is processed along the boundary of a text block, thereby creating a gradual dilated transition region. Edge-enhanced error diffusion using different multiplicative parameters is then applied to these regions. Meanwhile, dot-elimination artifacts are dot-disappearing phenomena occurring around high-frequency regions due to the characteristic of the edge-enhanced error diffusion to sharpen edge regions more. Thus, an error scaling factor is inserted in front of the error filter in the architecture of the edge-enhanced error diffusion to scale down the propagation errors. Experiments demonstrate that text readability is improved by increasing the sharpness of the text regions, and a less grainy appearance is simultaneously achieved compared with conventional edge-enhanced error diffusion in the background regions.

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Section: A Comparison Of the Halftone Resultsmentioning

confidence: 99%

mentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Text-Enhanced Error Diffusion Using Multiplicative Parameters and Error Scaling Factor

Jae-Hyun¹,

Son²,

Cho³

et al. 2006

jist

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“…With the development of optical information processing technology, the edge enhancement technology has been widely used in many fields such as optical imaging [1][2][3], fingerprint detection [4,5], astronomical observation [6,7] and so on. Although several new types of edge enhancement algorithms [8][9][10][11] and technologies [12][13][14][15] emerge in academic and industry, the disadvantages of poor sensitivity, low efficiency and high price in the information collection of infrared band are still existed. Thus methods for edge enhancement in the infrared band are urgently needed.…”

Section: Introductionmentioning

confidence: 99%

Implementing the edge enhancement with vortex filter in both linear and nonlinear optics

Xu,

Zhang

2023

Front. Phys.

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The edge enhancement technique, as an effective method to represent the boundary of objects, plays an important role in image processing. Among them, the vortex filtering, which is based on the radial Hilbert transformation, has been paid great attention due to its ability to achieve isotropic and anisotropic edge enhancement. Recent years have witnessed a growing interest in the nonlinear vortex filter to skillfully realize the visualization of the object edge under invisible light irradiation. In this paper, we start from reviewing the achievements have been made with the vortex filtering technique in linear optics, and then discussed the recent processes of the scalar and vector vortex filter in nonlinear optics. We hope that the nonlinear optical vortex filter can motivate some promising applications in biological edge imaging with visible light-sensitive specimens.

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“…However, error diffusion is built to balance the average gray level of image, so the edge of image will be blurring. To make this better, Lai and Chen [5] suggested vector error diffusion which uses different filter at each color space to enhance the edge of halftone images. This method reduces worm effect of background and enhances the edge of color halftone image.…”

Section: Introductionmentioning

confidence: 99%

Edge Enhancement Color Halftoning by Using IEE

Kwak

Kwon

Kim

et al. 2006

2006 International Forum on Strategic Technology

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Among the digital halftoning, the error diffusion halftoning gives better subjective quality than other halftoning techniques. But it also makes edges of objects blurred. To overcome the defect, this thesis proposes the modified error diffusion halftoning algorithm to enhance the edges using the spatial perceptual properties of the human visual system. Using the properties that the human eyes perceive not the pixel's luminance itself but the local average luminance and that information that human eyes perceive spatial variation, the proposed method computes information of edge enhancement(IEE). The IEE is added to the quantizer's input pixel and feeds into the halftoning quantizer. The quantizer produces the halftone image having the enhanced edge. Also this thesis proposes the technique that the coefficients of the error diffusion filter are adapted according to the correlation among color components. The computer simulation results show that the proposed method produces finer halftoning images than conventional methods due to the enhanced edges. And the proposed method also preserves similar in edges to original image and reduces some defects such as color impulse and false contours.

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Algorithms of halftoning color images with edge enhancement

Cited by 8 publications

References 13 publications

Text-Enhanced Error Diffusion Using Multiplicative Parameters and Error Scaling Factor

Text-Enhanced Error Diffusion Using Multiplicative Parameters and Error Scaling Factor

Implementing the edge enhancement with vortex filter in both linear and nonlinear optics

Edge Enhancement Color Halftoning by Using IEE

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