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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01 Jan 2010
TL;DR: A new robust hybrid watermarking technique based on recently introduced contourlet transform and singular value decomposition that shows the higher imperceptibility and robustness against common image processing attacks such as Jpeg compression, Jpeg 2000 compression, resizing, median filtering, histogram equalization, sharpening, and Gama correction.
Abstract: In this paper, we propose a new robust hybrid watermarking technique based on recently introduced contourlet transform and singular value decomposition. After applying the contourlet transform for the original image, we select the low frequency directional sub band coefficients and apply singular value decomposition. The singular values of the original image are then modified by the singular values of contourlet transformed binary logo watermark image. Combination of these two transforms on image improves the performance of watermarking algorithm. It has been observed that, in comparison with other contourlet based and wavelet based methods, the proposed method shows the higher imperceptibility and robustness against common image processing attacks such as Jpeg compression, Jpeg 2000 compression, resizing, median filtering, histogram equalization, sharpening, and Gama correction.
20 citations
TL;DR: A new robust image watermarking based on EMs invariants in nonsubsampled contourlet transform (NSCT) domain is proposed and the digital watermark is embedded by quantizing the modulus of the selected EMs.
20 citations
TL;DR: Experimental results show that compared to block compressed sensing with smooth projected Landweber (BCS-SPL), the proposed algorithm is much better with simple texture images and even complicated texture images at the same sampling rate.
20 citations
TL;DR: A novel single image super-resolution method based on progressive-iterative approximation that significantly outperforms the state-of-the-art methods in terms of both subjective and objective measures is proposed.
Abstract: In this paper, a novel single image super-resolution (SR) method based on progressive-iterative approximation is proposed. To preserve textures and clear edges, the image SR reconstruction is treated as an image progressive-iterative fitting procedure and achieved by iterative interpolation. Due to different features in different regions, we first employ the nonsubsampled contourlet transform (NSCT) to divide the image into smooth regions, texture regions, and edges. Then, a hybrid interpolation scheme based on curves and surfaces is proposed, which differs from the traditional surface interpolation methods. Specifically, smooth regions are interpolated by the non-uniform rational basis spline (NURBS) surface geometric iteration. To retain textures, control points are increased, and the progressive-iterative approximation of the NURBS surface is employed to interpolate the texture regions. By considering edges in an image as curve segments that are connected by pixels with dramatic changes, we use NURBS curve progressive-iterative approximation to interpolate the edges, which sharpens the edges and can maintain the image edge structure without jaggy and block artifacts. The experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of both subjective and objective measures.
20 citations
10 Oct 2005
TL;DR: This paper focuses on the text-independent writer identification based on off-line Chinese handwriting and presents a new contourlet-based GGD (Generalized Gaussian Density) method, which achieves a good experiment result in the authors' experiments.
Abstract: Handwriting-based writer identification is a hot research topic in the field of pattern recognition. Typically, there are four modes of writer identification: on-line text-dependent, on-line text-independent, off-line text-dependent, off-line text-independent; and off-line text-independent is the most challenging problem among them because many valuable writing features are not available in this case, such as shape features, dynastic writing information and etc. In this paper, we focus on the text-independent writer identification based on off-line Chinese handwriting and present a new contourlet-based GGD (Generalized Gaussian Density) method. This novel method achieves a good experiment result in our experiments.
20 citations