Topic
Contourlet
About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.
Papers published on a yearly basis
Papers
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TL;DR: In applications on nonlinear approximation, image coding, and denoising, the proposed filter banks show visual quality improvements and have higher PSNR than the conventional separable WT or the contourlet.
Abstract: In this paper, effective multiresolution image representations using a combination of 2-D filter bank (FB) and directional wavelet transform (WT) are presented. The proposed methods yield simple implementation and low computation costs compared to previous 1-D and 2-D FB combinations or adaptive directional WT methods. Furthermore, they are nonredundant transforms and realize quad-tree like multiresolution representations. In applications on nonlinear approximation, image coding, and denoising, the proposed filter banks show visual quality improvements and have higher PSNR than the conventional separable WT or the contourlet.
34 citations
TL;DR: ShearLab 3D as discussed by the authors is a CUDA-based universal shearlet system for 2D and 3D denoising, inpainting, and feature extraction.
Abstract: Wavelets and their associated transforms are highly efficient when approximating and analyzing one-dimensional signals. However, multivariate signals such as images or videos typically exhibit curvilinear singularities, which wavelets are provably deficient of sparsely approximating and also of analyzing in the sense of, for instance, detecting their direction. Shearlets are a directional representation system extending the wavelet framework, which overcomes those deficiencies. Similar to wavelets, shearlets allow a faithful implementation and fast associated transforms. In this paper, we will introduce a comprehensive carefully documented software package coined ShearLab 3D (www.ShearLab.org) and discuss its algorithmic details. This package provides MATLAB code for a novel faithful algorithmic realization of the 2D and 3D shearlet transform (and their inverses) associated with compactly supported universal shearlet systems incorporating the option of using CUDA. We will present extensive numerical experiments in 2D and 3D concerning denoising, inpainting, and feature extraction, comparing the performance of ShearLab 3D with similar transform-based algorithms such as curvelets, contourlets, or surfacelets. In the spirit of reproducible reseaerch, all scripts are accessible on www.ShearLab.org.
34 citations
TL;DR: RNAMlet as discussed by the authors uses non-symmetry anti-packing pattern representation model (NAM) to decompose the image into a set of rectangular blocks asymmetrically according to gray value changes of image pixels.
34 citations
TL;DR: Experimental results show that the TIDFT outperforms some other frame-based Denoising methods, such as contourlet and shearlet, and is competitive to the state-of-the-art denoising approaches.
Abstract: This paper is devoted to the study of a directional lifting transform for wavelet frames. A nonsubsampled lifting structure is developed to maintain the translation invariance as it is an important property in image denoising. Then, the directionality of the lifting-based tight frame is explicitly discussed, followed by a specific translation invariant directional framelet transform (TIDFT). The TIDFT has two framelets ψ1, ψ2 with vanishing moments of order two and one respectively, which are able to detect singularities in a given direction set. It provides an efficient and sparse representation for images containing rich textures along with properties of fast implementation and perfect reconstruction. In addition, an adaptive block-wise orientation estimation method based on Gabor filters is presented instead of the conventional minimization of residuals. Furthermore, the TIDFT is utilized to exploit the capability of image denoising, incorporating the MAP estimator for multivariate exponential distribution. Consequently, the TIDFT is able to eliminate the noise effectively while preserving the textures simultaneously. Experimental results show that the TIDFT outperforms some other frame-based denoising methods, such as contourlet and shearlet, and is competitive to the state-of-the-art denoising approaches.
34 citations
TL;DR: This method is presented a blind and robust watermarking method to copyright protection in digital video using the singular value decomposition (SVD) and pseudo-random numbers generated by the proposed new chaotic map, which is a generalized two-dimensional complex map based on the Newton model.
Abstract: The rapid growth of fast communication networks for digital video transmission has created a need to copyright protection for these media. Digital video can be manipulated easily by users with various motivations. Compression is the most common attack that users can apply on videos in order to eliminate digital video copyright. Proposed technique in this article is specially designed for resisting against compression attacks. This method is presented a blind and robust watermarking method to copyright protection in digital video. In the proposed method, the coefficients of the contourlet transform are extracted and then encrypted watermark embedded into video with using the singular value decomposition (SVD). Embedding watermark in SVD domain increases the robustness of proposed method against attacks. In the embedding process and watermark encryption, pseudo-random numbers generated by the proposed new chaotic map, which is a generalized two-dimensional complex map based on the Newton model. The PSNR, SSIM, BER, and NCC measures examine the performance of the proposed method in terms of robustness and visual quality.
34 citations