Topic
Contourlet
About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.
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TL;DR: Experimental results show that the optimum values of scale and direction parameters are respectively 2 and 3, and the matching accuracy of the proposed matching cost is twice higher than that of traditional NCC cost.
9 citations
Journal Article•
TL;DR: Experimental results show that the new algorithm is an effective method that introduces more detailed information while preserves power structure.
Abstract: Edge-based image fusion algorithm can not preserve energy structure which is important in indicating the reflection feature of the observed objects.This paper proposed an enhanced algorithm considering both edge and energy structures.Nonsubsampled contourlet transform is firstly deployed to analyze input image in both multi-scale and multi-direction sub-bands.Low frequency band is then divided into contour and smooth regions by edge energy.Edge energy is used as a fusion judgment for the contour region while local energy and correlation are selected as judgments for the smooth region.Local variance and correlation are chosen to merge the coefficients of high frequency sub-bands.Inverse NSCT transform is finally used to get the fused image.Experimental results show that the new algorithm is an effective method that introduces more detailed information while preserves power structure.
9 citations
12 Dec 2008
TL;DR: Experimental results show the imperceptibility and high robustness of the proposed method against Additive White Gaussian Noise (AWGN) and JPEG compression attacks.
Abstract: In this paper, a new multiplicative image watermarking system is presented. As human visual system is less sensitive to the image edges, watermarking is applied in the contourlet domain, which represents image edges sparsely. In the presented scheme, watermark data is embedded in the most energetic directional subband. By modeling general gaussian distribution (GGD) for the contourlet coefficients, the distribution of watermarked noisy coefficients is analytically calculated. At the receiver, based on the maximum likelihood (ML) decision rule, the optimal detector is proposed. Experimental results show the imperceptibility and high robustness of the proposed method against Additive White Gaussian Noise (AWGN) and JPEG compression attacks.
9 citations
26 Mar 2006
TL;DR: A contextual hidden Markov model, which was successfully applied to wavelet image denoising, has been adapted into the contourlet domain and the resulting contourlets contextual HMM has been tested in a Denoising application with promising results, which verified its effectiveness in characterizing contourlett images.
Abstract: The contourlet transform is a recently developed two-dimensional transform technique. It is reported to be more effective than wavelets in representing smooth curvature details typical of natural images. To fully exploit the potential of contourlets in image processing and analysis applications, appropriate models are needed to describe statistical characteristics of images in the contourlet domain. In this paper, statistical contourlet image modeling techniques have been investigated. A contextual hidden Markov model, which was successfully applied to wavelet image denoising, has been adapted into the contourlet domain. The resulting contourlet contextual HMM has been tested in a denoising application with promising results, which verified its effectiveness in characterizing contourlet images
9 citations
Patent•
29 Dec 2010
TL;DR: In this article, a multiscale SAR image segmentation method based on semi-supervised learning is proposed, which overcome the disadvantages of low segmentation accuracy and relatively long operation time of the traditional segmentation methods.
Abstract: The invention discloses a multiscale SAR image segmentation method based on semi-supervised learning, belonging to the technical field of image processing and mainly overcoming the disadvantages of low segmentation accuracy and relatively long operation time of the traditional segmentation methods. The implementation steps are as follows: (1) three-layer wavelet transform and three-layer Contourlet transform are respectively carried out on the images to be segmented to finish image decomposition and a coarse decomposition subband, a sub-coarse decomposition subband and a fine decomposition subband are obtained by merge operation; (2) with respect to the coarse decomposition subband, the method of semi-supervised learning is adopted to finish initial segmentation and obtain the results ofinitial segmentation; and (3) multiscale secondary segmentation based on unsupervised learning is carried out on the results of initial segmentation, the sub-coarse decomposition subband and the finedecomposition subband obtained in step (1) to obtain the final segmentation result. The method improves the accuracy of the segmented images, reduces the misclassification rate and can be used for texture image segmentation, natural image segmentation and medical image segmentation.
9 citations