Contourlet

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

CBS: Contourlet-Based Steganalysis Method

Abstract: An ideal steganographic technique embeds secret information into a carrier cover object with virtually imperceptible modification of the cover object. Steganalysis is a technique to discover the presence of hidden embedded information in a given object. Each steganalysis method is composed of feature extraction and feature classification components. Using features that are more sensitive to information hiding yields higher success in steganalysis. So far, several steganalysis methods have been presented which extract some features from DCT or wavelet coefficients of images. Multi-scale and time-frequency localization of an image is offered by wavelets. However, wavelets are not effective in representing the images in different directions. Contourlet transform addresses this problem by providing two additional properties, directionality and anisotropy. The present paper offers an universal approach to steganalysis called CBS, which uses statistical moments of contourlet coefficients as features for analysis. After feature extraction, a non-linear SVM classifier is applied to classify cover and stego images. The efficiency of the proposed method is demonstrated by experimental investigations. The proposed steganalysis method is compared with two well-known steganalyzers against typical steganography methods. The results showed the superior performance of our method.

Robust feature points extraction for image registration based on the nonsubsampled contourlet transform

Abstract: In this letter, a new feature points extraction method based on the nonsubsampled contourlet transform (NSCT) is proposed for image registration. The primary motivation of this work is to determine the effectiveness of the NSCT transform in extracting feature points for image registration. Preliminary experimental results show that the registration accuracy and robustness of the proposed algorithm is acceptable and very promising, and confirm the success of the proposed NSCT-based feature points extraction approach.

Technique for image fusion based on non-subsampled contourlet transform domain improved NMF

Abstract: To overcome the problem of multi-sensor image fusion, a technique for image fusion based on non-subsampled contourlet transform (NSCT) domain improved nonnegative matrix factorization (NMF) is presented. Firstly, by using NSCT, multi-scale and multi-direction sparse decompositions of source images are performed. Then, an improved NMF technique is utilized to complete the fusion of low-frequency sub-images. The low-frequency fused image can be produced fast by the process which does not involve the randomization of the vectors W and H at all, in addition, the fusion course of high-frequency sub-images can be dealt with by use of the model of adaptive unit-fast-linking pulse coupled neural network (AUFLPCNN). Finally, the ultimate fused image can be obtained by synthesizing all sub-images with inverse NSCT. The simulated experiments show that the technique is effective.

Contourlet domain image denoising using normal inverse gaussian distribution

Abstract: A new contourlet-based method is introduced for reducing noise in images corrupted by additive white Gaussian noise It is shown that a symmetric normal inverse Gaussian distribution is more suitable for modeling the contourlet coefficients than formerly-used generalized Gaussian distribution To estimate the noise-free coefficients, a Bayesian maximum a posteriori estimator is developed utilizing the proposed distribution In order to estimate the parameters of the distribution, a moment-based technique is used The performance of the proposed method is studied using typical noise-free images corrupted with simulated noise and compared with that of the other state-of-the-art methods It is shown that compared with other denoising techniques, the proposed method gives higher values of the peak signal-to-noise ratio and provides images of good visual quality

Polarized SAR image classification method based on nonsubsampled contourlet convolutional neural network

Abstract: The invention discloses a polarized SAR image classification method based on a nonsubsampled contourlet convolutional neural network, and mainly at solving the problems that influence of speckle noises is hard to avoid and the classification precision is low in the prior art. The method comprises the steps that a polarized SAR image to be classified is denoised; Pauli decomposition is carried out on a polarized scattering matrix S obtained by denoising; image characteristics obtained via Pauli decomposition are combined into a characteristic matrix F, and the characteristic matrix F is normalized and recorded as F1; 22*22 blocks surrounding the F1 are taken for each pixel point to obtain a block based characteristic matrix F2; a training data set and a test data set are selected from the F2; the nonsubsampled contourlet convolutional neural network is established to train the training data set; and the trained nonsubsampled contourlet convolutional neural network is used to classify the test data set. The polarized SAR image classification method improves the expression capability and the classification precision of the features of the polarized SAR image, and can be used for target identification.