Sharpness Enhancement Algorithm through Edge Information

Tai, Shen-Chuan; Chen, Zih-Siou; Chang, Yu-Chieh; Liao, Tzu-Wen; Chen, Juo-Chen

doi:10.1109/icicta.2012.121

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Finally, the contrast will be enhanced by using an adaptive histogram equalization approach to get a better visual perceptual quality [ 11 ]. In [ 12 ], an edge-weighted contrast enhancement algorithm is proposed. The image to be enhanced is first convolved with a median filter to get a low-pass filtered image.…”

Section: Introductionmentioning

confidence: 99%

A Three-Step Approach with Adaptive Additive Magnitude Selection for the Sharpening of Images

Kau

Lee

2014

The Scientific World Journal

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Aimed to find the additive magnitude automatically and adaptively, we propose a three-step and model-based approach for the sharpening of images in this paper. In the first pass, a Grey prediction model is applied to find a global maximal additive magnitude so that the condition of oversharpening in images to be sharpened can be avoided. During the second pass, edge pixels are picked out with our previously proposed edge detection mechanism. In this pass, a low-pass filter is also applied so that isolated pixels will not be regarded as around an edge. In the final pass, those pixels detected as around an edge are adjusted adaptively based on the local statistics, and those nonedge pixels are kept unaltered. Extensive experiments on natural images as well as medical images with subjective and objective evaluations will be given to demonstrate the usefulness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

A Three-Step Approach with Adaptive Additive Magnitude Selection for the Sharpening of Images

Kau

Lee

2014

The Scientific World Journal

View full text Add to dashboard Cite

show abstract

“…Finally, the contrast will be enhanced by using a proposed adaptive HE approach to get a better visual perceptual quality [ 12 ]. In [ 5 ], an edge-weighted contrast enhancement algorithm is proposed. The image to be enhanced is first convolved with a median filter to get a low-pass filtered image.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Self‐Adapted Approach to the Sharpening of Color Images

Kau

Lee

2013

The Scientific World Journal

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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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Sharpness Enhancement Algorithm through Edge Information

Cited by 2 publications

References 9 publications

A Three-Step Approach with Adaptive Additive Magnitude Selection for the Sharpening of Images

A Three-Step Approach with Adaptive Additive Magnitude Selection for the Sharpening of Images

An Efficient and Self‐Adapted Approach to the Sharpening of Color Images

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