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Contourlet

About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.


Papers
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Proceedings ArticleDOI
30 Dec 2008
TL;DR: Wang et al. as mentioned in this paper employed a cycle spinning (CS) method to improve the denoising performance of SFLCT by averaging out the translation dependence, and the proposed CS-SFLCT outperformed the original contourlet transform in terms of PSNR and in visual effects.
Abstract: Contourlet transform provides flexible number of directions and captures the intrinsic geometrical structure of images. The efficient directional filter banks with low redundancy of contourlet are very attractive for image processing. However, non-ideal filters are used in the original contourlet transform, especially when combined with laplacian pyramid, which results in pseudo-Gibbs phenomena around singularities for image denoising. Sharp frequency localized contourlet transform (SFLCT) is a new construction contourlet to overcome this drawback by replacing the laplacian pyramid with a new multiscale decomposition which significantly improve the denoising performance than the original form. Unfortunately, the downsampling of SFLCT makes it lack translation invariance. Thus, we employ a cycle spinning (CS) method to improve the denoising performance of SFLCT, named as cycle spinning based SFLCT (CS-SFLCT), by averaging out the translation dependence. Experimental results demonstrate that the proposed CS-SFLCT outperforms SFLCT, contourlet and cycle spinning-based contourlet for denoising in terms of PSNR and in visual effects.

12 citations

Journal ArticleDOI
TL;DR: A new fusion method based on nonsampled contourlet transform (NSCT) is proposed and it is shown that this approach can achieve better results than other fusion methods.
Abstract: A novel image fusion algorithm based on nonsubsampled contourlet transform (NSCT) and spiking cortical model (SCM) is proposed in this paper, aiming at solving the fusion problem of multifocus images. The fusion rules of subband coefficients of NSCT are discussed, and a new maximum selection rule (MSR) is defined to fuse low frequency coefficients instead of using traditional MSR directly. For the fusion rule of high frequency coefficients, spatial frequency (SF) of each high frequency subband is considered as the gradient features of images to motivate SCM networks and generate pulse of neurons, and then the time matrix of SCM is set as criteria to select coefficients of high frequency subband. Experimental results and visual evaluation demonstrate the effectiveness of the proposed fusion method. Objective tests and analysis conducted under different noised source image environments proved the robustness of the proposed fusion method.

12 citations

Journal Article
TL;DR: A watermarking algorithm that uses the wavelet transform with Multiple Description Coding (MDC) and Quantization Index Modulation (QIM) concepts is introduced and is found to be robust against different attacks and has good results compared to the contourlet-based algorithm.
Abstract: In this paper, a watermarking algorithm that uses the wavelet transform with Multiple Description Coding (MDC) and Quantization Index Modulation (QIM) concepts is introduced. Also, the paper investigates the role of Contourlet Transform (CT) versus Wavelet Transform (WT) in providing robust image watermarking. Two measures are utilized in the comparison between the waveletbased and the contourlet-based methods; Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NCC). Experimental results reveal that the introduced algorithm is robust against different attacks and has good results compared to the contourlet-based algorithm. Keywords—image watermarking; discrete wavelet transform; discrete contourlet transform; multiple description coding; quantization index modulation.

12 citations

Proceedings ArticleDOI
14 May 2011
TL;DR: The application of contourlet transform in conjunction with LPP is introduced to overcome limitations and Experimental results on the ORL, Yale, YaleB, CMU PIE face database show the effectiveness of thecontourlet-based locality preserving projection (CLPP) method.
Abstract: Locality preserving projection (LPP) is a successful method in face recognition for feature extraction. However, the recognition efficiency of LPP technique is often degraded by the very high dimensional nature of the image space. It is difficult to calculate the bases to represent the original facial images. So the algorithm describing image in vector form is often applied in data after dimension reduction by PCA which result in the algorithm sensitive to how to estimate the intrinsic dimensionality of the nonlinear face manifold in the PCA preprocessing step. A novel approach is presented in this paper to avoid the difficulty. We introduce the application of contourlet transform in conjunction with LPP to overcome these limitations. Experimental results on the ORL, Yale, YaleB, CMU PIE face database show the effectiveness of the contourlet-based locality preserving projection (CLPP) method.

12 citations

Proceedings ArticleDOI
01 Sep 2009
TL;DR: A 3D face recognition paradigm that bypasses reconstruction and exploits the plethora of information available in multiple images of a person acquired while varying the illumination is proposed.
Abstract: Three-dimensional face recognition is illumination invariant, however the acquisition process itself is not. In active 3D recognition, multiple images are captured while the face is actively illuminated with different patterns. We propose a 3D face recognition paradigm that bypasses reconstruction and exploits the plethora of information available in multiple images of a person acquired while varying the illumination. Illumination is varied by scanning a horizontal and then a vertical white stripe on the computer screen in front of the subject. Subtracting ambient light leaves images illuminated by the screen from different angles. The contourlet coefficients of the images are calculated at different scales and orientations and then projected to PCA subspace to remove redundancy. The subspace contourlet coefficients of multiple images are stacked to form a global face representation. Sliding windows are used during matching to remove the disparity between the face locations with respect to the screen. The proposed algorithm was tested under varying ambient conditions and compared to a known 3D face recognition technique. Verification results on data from the same subjects show the strength of our algorithm.

12 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202336
202299
202175
2020109
2019155
2018164