Contourlet

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

Contourlet-Based Acoustic Seabed Ground Discrimination System

Abstract: In this paper, the problem of automatic segmentation and classification of seafloor using acoustic ground discrimination systems is discussed and a new split and merge algorithm based on contourlet transform is presented. The contourlet transform is a new two-dimensional extension of the wavelet transform using multiscale and directional filter banks. It appears to be a suitable tool for this task, because it allows analysis of images at various levels of resolution as well as directions, which effectively capture smooth contours that are the dominant feature in seabed images. In the proposed approach, the input image of seabed is split into M times M blocks. Each block is classified separately using four levels of contourlet transform with 28 directional bandpass sub-bands. First-order and second-order statistics of the high frequency details from all sub-bands of the contourlet coefficients are extracted as features and a weighted distance classifier is used for block classification. Moreover, the effectiveness of other contourlet domain features such as sub-band energy in discriminating different seabed textures is presented. Finally, the classified blocks on the boundaries of the segmented image, are refined and merged to produce the final segmented image. The proposed method provides a fast tool with enough accuracy that can be implemented in a parallel structure for real-time processing. In addition, the simulation results are compared with the results of wavelet-based methods as well as other well-known techniques to show the effectiveness of our proposed algorithm.

A New Method for Remote Sensing Image Fusion Based on Nonsubsampled Contourlet Transform

Abstract: A new method for remote sensing image fusion based on Non Subsampled Contourlet Transform(NSCT) is presented.The NSCT is a shift-invariant directional wavelet transform approach.Multiresolution analysis of the image results in several high-pass subbands representing efficiently the detailed information of the image.Combined with the HIS transform,NSCT injects details into multispectral images.Therefore,the merged images obtained from NSCT not only has high spatial resolution but also keeps the high fidelity of spectral characteristics.Experimental results for Real SPOT panchromatic image and TM multispectral images show that the performances of proposed method are better than recently wide-used wavelet domain methods,such as Discrete Wavelet Transform,A Trous Transform and Contourlet Transform,etc.

Contourlet-based despeckling for SAR image using hidden Markov tree and Gaussian Markov models

Abstract: The granular appearance of speckle noise in synthetic aperture radar (SAR) imagery makes it very difficult to visually and automatically interpret SAR data. Therefore, speckle reduction is a prerequisite for many SAR image processing tasks. In this paper, we propose a contourlet-based despeckling method for the SAR image using the hidden Markov tree (HMT) and Gaussian Markov models. The contourlet transform is a recently developed two-dimensional transform technique. It is reported to be more effective than wavelets in representing smooth curvature details typical of natural images. The HMT and Gaussian Markov models will reflect the correlations of the contourlet coefficients not only across scales and directions but also between neighbors. The experimental results show that the proposed method in contrary to other methods can obtain a better trade-off between smoothing the homogeneous areas and keeping the edges and can get better visual effect.