Contourlet

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

Contourlet domain multi-modal medical image fusion method based on statistical modeling

Abstract: The invention discloses a Contourlet domain multi-modal medical image fusion method based on statistical modeling, mainly for solving the problems of difficulty in balancing spatial resolution and spectrum information during medical image fusion. The realization steps comprise: 1), performing IHS transformation on an image to be fused, and obtaining brightness, tone and saturation; 2), respectively executing Contourlet transformation on a brightness component, and estimating the CHMM parameters of a context hidden Markov model of a high frequency sub-band by use of an EM algorithm; 3), a low frequency sub-band employing a fusion rule of taking the maximum from area absolute value sums, and the high frequency sub-band designing a fusion rule based on a CHMM and an improved pulse coupling nerve network M-PCNN; 4), a high frequency coefficient and a low frequency coefficient after fusion executing Contourlet inverse transformation to reconstruct a new brightness component; and 5), obtaining a fusion image by use of IHS inverse transformation. The method provided by the invention can fully integrate the structure and function information of a medical image, effectively protects image details, improves the visual effect, and compared to a conventional fusion method, greatly improves the quality of a fusion image.

A Novel Statistical Detector for Contourlet Domain Image Watermarking Using 2D-GARCH Model

Abstract: In this paper, we propose a novel watermark detector in contourlet domain using likelihood ratio test (LRT). Since the accuracy of an LRT based watermark detector is dependent on the efficiency of the applied statistical model, first, we study the statistical properties of the contourlet coefficients. Using different tests, we demonstrate that the marginal distribution of contourlet coefficients is heavy-tailed and heteroscedasticity exists in these coefficients. All of the previously proposed models for contourlet coefficients assume that these coefficients are identically distributed, so they can not capture the characteristics of the contourlet coefficients. To overcome this problem, we propose using two dimensional generalized autoregressive conditional heteroscedasticity (2D-GARCH) model for contourlet coefficients that provides an efficient structure for the dependencies of these coefficients. Based on using 2D-GARCH model, a novel LRT based heteroscedastic watermark detector is designed in contourlet domain. Experimental results confirm the efficiency of the proposed watermark detector under different types of attacks and its outperformance compared with alternative watermarking methods.

Refined Pan-Sharpening With NSCT and Hierarchical Sparse Autoencoder

Abstract: Most of available pan-sharpening technologies suffer from spectral and spatial distortions, for the coarse extraction from Panchromatic (Pan) image and brute injection of details to multispectral (MS) images. In this paper, in order to reduce the color distortion and enhance the spatial information of fused images, we propose a refined pan-sharpening (RPS) method using geometric multiscale analysis (GMA) and hierarchical sparse autoencoder (HSAE). First, a GMA tool, nonsubsampled contourlet transform (NSCT), is used to capture directional details of the Pan image at multiple scales. Then at each scale, HSAE is developed to gradually filter out the refined spatial details, via sparsely coding details under spatial self-dictionaries. The refined details are then injected into MS images to alleviate spectral distortions. By exploring the spatial structure in images and refining the spatial details injection via HSAE, RPS can reduce distortions to present fidelity colors and sharp appearance. Some experiments are taken on several datasets collected by QuickBird, Geoeye, and IKONOS satellites, and the experimental results show that RPS can reduce distortions in both the spectral and spatial domains, and outperform some related methods in terms of both visual results and numerical guidelines.

Synthesized pansharpening using curvelet transform and adaptive neuro-fuzzy inference system

Abstract: Image fusion is an important technique in remote sensing to improve visual interpretation and classification. Pansharpening is the procedure of fusing panchromatic (PAN) and multispectral images to produce high spatial and spectral resolution images. Synthesized pansharpening is performed on Linear Imaging Self-Scanning Sensor III and Advanced Wide Field Sensor data, which are freely available and provided by the National Remote Sensing Center. The Adaptive Neuro-Fuzzy Inference System (ANFIS) in multiscale transform domain for multisensor image fusion application is evaluated. The state-of-the-art method has been evaluated by various quality metrics. The computational cost of ANFIS with wavelet, contourlet, shearlet, and curvelet transform is investigated. This study proves that curvelet with ANFIS-based fusion technique outperformed state-of-the-art techniques. The application will be used to incorporate the missing spectral information in the high spatial resolution PAN image to identify objects, highlighting the regions clearly.

Contourlet coefficient modeling with generalized Gaussian distribution and application

Abstract: The contourlet transform can effectively provide sparse and decorrelated image representation. And its subband coefficients can be modeled as the generalized Gaussian (GG) distribution. In this paper, an improved maximum likelihood (ML) parameter estimation method is proposed, in which a novel initial estimation value and a modified iterative algorithm are used. The new approach has been applied to the contourlet-based texture image retrieval. Experimental results show that, compared with the current ML estimation method, the proposed approach can more accurately estimate the GG distribution parameters, and more effectively improve average retrieval rate on the VisTex database of 640 texture images.