Study of Technique of Edge Detection Based on Curvelet Transform

Liu, Zhenghai; Jian-xiong, Qiu

doi:10.1109/iscid.2009.142

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…In this work, we detect the image edges similar to [20] for the blue channel. The rule for partitioning the scale level is: 3 ) ( 2 log scale   n where the parameter n for any square size image refers to the number of rows.…”

Section: Proposed Methodsmentioning

confidence: 99%

Using Curvelet transform for watermarking based on amplitude modulation

Lari

Ghofrani

McLernon

2013

SIViP

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In this paper we propose a precise and robust watermarking scheme based on the technique called amplitude modulation. A watermark is embedded in a color image by modifying the pixel values in the blue channel. At the receiver, the watermark bits are retrieved using a prediction system, by a linear combination of nearby pixel values around the embedded pixels, and without having the original image. Because amplitude modulation is a spatial-domain watermarking method, it may not be robust enough, i.e. incapable of exact watermark retrieval. In order to enhance the bit retrieval, we apply a Gaussian mask to equalize the luminance intensity; we employ the pixel value replacing technique to enhance the prediction performance; and we use two additional bits as a geometrical reference. In addition, we demonstrate that choosing an improper location (like singularities) for watermarking will lead the prediction system to malfunction. In order to increase the robustness, we propose using the Curvelet transform to detect singularities such as lines and curves and prevent the system from using these locations in an image for embedding the watermark bits. The experimental results indicate that our proposed method has a better performance in comparison with two other similar approaches and in addition it is robust against various geometrical and non-geometrical attacks as well as having a good imperceptibility.

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Section: Proposed Methodsmentioning

confidence: 99%

Using Curvelet transform for watermarking based on amplitude modulation

Lari

Ghofrani

McLernon

2013

SIViP

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“…On the contrary of authors of [15,16], who use Curvelet transform to improve the quality of original image before using gradient edge detector to extract contour, we start our algorithm by contour detection and the Curvelet transform is called upon to improve the contour detected. Canny edge detector [21] was used in our algorithm to obtain first contours.…”

Section: Edge Improvement Using Curvelet Transformmentioning

confidence: 99%

“…The edges are obtained using the non-maximal suppression and hysteresis thresholding of the Canny algorithm. In order to enhance the edges of an image Liu and Qiu have used different means to deal with different scales of the coefficients to enhance the edge of image [15]. The best recent edge detectors through traditional methods start by improving the image quality with Curvelet transform [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…In order to enhance the edges of an image Liu and Qiu have used different means to deal with different scales of the coefficients to enhance the edge of image [15]. The best recent edge detectors through traditional methods start by improving the image quality with Curvelet transform [15,16]. The analysis of Curvelet coefficients contributions in image processing can found in [15,16,18].…”

Section: Introductionmentioning

confidence: 99%

“…The best recent edge detectors through traditional methods start by improving the image quality with Curvelet transform [15,16]. The analysis of Curvelet coefficients contributions in image processing can found in [15,16,18]. Unfortunately authors have not been interested enough on local structure of Curvelet coefficients contributions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Interest Points of Curvelet Coefficients Contributions of Microscopic Images and Improvement of Edges

Djimeli¹,

Tchiotsop²,

Tchinda³

2013

SIPIJ

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This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study local structure in images. The permutation of Curvelet coefficients from original image and edges image obtained from gradient operator is used to improve original edges. Experimental results show that this method brings out details on edges when the decomposition scale increases.

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Edge detection in noisy image using kernel regression

Xiong

Fang

2012

2012 International Conference on Wavelet Analysis and Pattern Recognition

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Edge detection is one of the most commonly used operations in image analysis. Most algorithms contain two basic steps: denoise and derivative computing. We apply kernel regression to remove noise and to get gray-level and derivative intensity surface of images. We explore the Nadaraya-Watson kernel regression which conquers the more negative impact caused by noises for derivative computing than general algorithms. However it also smoothes the jump points which may be edge pixels in image. So we also present bilateral kernel regression to deal with the problem. Experiments are carried out for extracting edge information from real images, without and with the contamination of Gaussian white noise. For each sample image, edge is extracted under the two cases without noises and with the peak-signal-noise-ratio (PSNR) from 18.5 dB to 34 dB. The proposed algorithm is compared with several other existing methods, the Prewitt and Canny detectors. The experimental results indicate that our method has a better performance in noisy images than other methods.

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Study of Technique of Edge Detection Based on Curvelet Transform

Cited by 7 publications

References 6 publications

Using Curvelet transform for watermarking based on amplitude modulation

Using Curvelet transform for watermarking based on amplitude modulation

Analysis of Interest Points of Curvelet Coefficients Contributions of Microscopic Images and Improvement of Edges

Edge detection in noisy image using kernel regression

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