Topic
Contourlet
About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.
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TL;DR: The proposed NSCT-domain shrinkage estimator consists of a new likelihood ratio function and a new prior ratio function, both of which are dependent on the estimated masks for the NSCT coefficients, and is equipped with directional neighborhood configurations to accommodate the estimator to the flexible directionality of theNSCT, and thus to enhance the detail fidelity.
Abstract: A new spatially adaptive shrinkage approach based on the nonsubsampled contourlet transform (NSCT) to despeckling synthetic aperture radar (SAR) images is proposed. This method starts from the existing stationary wavelet transform (SWT)–domain Gamma-exponential likelihood model combined with a local spatial prior model and extends the model further for despeckling an SAR image via spatially adaptive shrinkage in the NCST domain. The proposed NSCT-domain shrinkage estimator consists of a new likelihood ratio function and a new prior ratio function, both of which are dependent on the estimated masks for the NSCT coefficients. The former is established by the Gamma distribution with variable scale and shape parameters and the exponential distribution with variable scale parameter to adapt the shrinkage estimator to the redundancy property of the NSCT. Parameters of these two distributions are estimated by using moment-based estimators. The latter is equipped with directional neighborhood configurations to accommodate the estimator to the flexible directionality of the NSCT, and thus to enhance the detail fidelity. We validate the proposed method on real SAR images and demonstrate the excellent despeckling performance through comparisons with the SWT-based counterpart, two classical spatial filters, and the contourlet transform-based despeckling technique.
15 citations
01 Jul 2019
TL;DR: Experimental results demonstrate the high efficiency of the textured image retrieval scheme, which can provide better retrieval rates and lower computational cost, in comparison with the state-of-the-art approaches recently proposed in the literature.
Abstract: In this paper, we proposed a new framework for textured image retrieval, which is based on Weibull statistical distribution and nonsubsampled contourlet transform. Firstly, the image is decomposed into one lowpass subband and several highpass subbands by using nonsubsampled contourlet transform (NSCT). Secondly, Weibull probability distribution is employed to describe the statistical characteristics of the highpass NSCT coefficients, and the Weibull model parameters are utilized to construct a compact texture image feature space. Finally, image similarity measurement is accomplished by using closed-form solutions for the Kullback–Leibler divergences between the Weibull statistical models. Experimental results demonstrate the high efficiency of our textured image retrieval scheme, which can provide better retrieval rates and lower computational cost, in comparison with the state-of-the-art approaches recently proposed in the literature.
15 citations
TL;DR: A new algorithm is proposed that optimally combines information from thermal images with a visual image of the same scene to create a single comprehensive fused image.
Abstract: Image fusion plays a vital role in providing better visualization of image data. In this paper, we propose a new algorithm that optimally combines information from thermal images with a visual image of the same scene to create a single comprehensive fused image. In this work, an improved version of particle swarm optimization alogithm is proposed to optimally combine the thermal and visible images. The proposed algorithm is named self tunning particle swarm optimization (STPSO). Because of the importance of the fusion rule, a weighted averaging fusion rule is formulated that uses optimal weights resulting from STPSO for the fusion of both high frequency and low frequency coefficients obtained by applying Dual Tree Discrete Wavelet Transform (DT-DWT). The objective function in STPSO is formulated with the twin objectives of maximizing the Entropy and minimizing the Root Mean Square Error (RMSE), which differentiates our work from existing fusion techniques. The efficiency of our fusion algorithm is also evaluated by adding Gaussian white noise to the source images. The fusion results are compared with existing multi-resolution based fusion techniques, such as Laplacian Pyramid (LAP), Discrete Wavelet Transform (DWT) and Non Sub-Sampled Contourlet Transform (NSCT). The simulation results indicate that the proposed fusion framework results in better quality fused images when evaluated with subjective and objective metrics. Comparision of these results with those from PSO shows that our algorithm outperforms generic PSO.
15 citations
Patent•
21 Sep 2011
TL;DR: In this article, a method for extracting roads from a remote sensing image, which belongs to the technical field of image processing and solves the problem that the existing technology is not precise in detection and positioning of roads, and has a large number of false targets and bad continuity.
Abstract: The invention discloses a method for extracting roads from a remote sensing image, which belongs to the technical field of image processing and solves the problem that the existing technology is not precise in detection and positioning of roads, and has a large number of false targets and bad continuity. The specific realization process comprises the following steps of: firstly implementing pretreatments including adaptive histogram equalization and Frost de-noising on the input images; then implementing three layers of non-sub-sampled contourlet transform thereon, decomposing each layer intoeight directions, extracting the model maximum value of each direction sub-band of the first layer and the second layer as the linear characteristic vectors of roads; clustering the obtained characteristic vectors by using fuzzy C means clustering algorithm to obtain the initial extraction results of roads; and finally implementing non maximum value inhibition and road post treatment based on thespatial relationship to the initial extraction to obtain the final road extraction result. The invention has the advantages of accurate road positioning, good integrality, low calculation complexity and no need of training and learning, and is used for analysis and processing of the remote sensing image.
15 citations
01 Oct 2007
TL;DR: Experimental results show that the contourlet features are very stable features for invariant palmprint classification, and better classification rates are reported when compared with other existing classification methods.
Abstract: In this paper, we propose a new palmprint classification method by using the contourlet features. The contourlet transform is a new two dimensional extension of the wavelet transform using multiscale and directional filter banks. It can effectively capture smooth contours that are the dominant features in palmprint images. AdaBoost is used as a classifier in the experiments. Experimental results show that the contourlet features are very stable features for invariant palmprint classification, and better classification rates are reported when compared with other existing classification methods.
15 citations