Contourlet

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

Image Retrieval using Contourlet Transform

Abstract: image retrieval problem has recently become more important and necessary because of the rapid growth of multimedia databases and digital libraries. Different search engines use different features to retrieve images from the database. In this paper, the Contourlet Transform is developed to retrieve similar images from the image database. By combining the Laplacian pyramid and the Directional Filter Bank (DFB), a new image representation is obtained. The direction subbands coefficients are used to form a feature vector for classification. The performance of the Contourlet Transform is evaluated using standard bench marks such as Precision and Recall. An experiment shows that the Contourlet Transform (CT) features provide the best results in Image Retrieval.

Method for reducing speckle noises of SAR image based on neighborhood directivity information

Abstract: The invention discloses a method for reducing speckle noises of an SAR image based on neighborhood directivity information, which mainly overcomes the defects that the prior method for reducing the speckle noises of the SAR image cannot inhibit the speckle noises well and lose part of marginal and detailed information. The method comprises the following steps: firstly, performing non-subsampled Contourlet decomposition on an original image, and estimating a binary mask to each high-frequency sub-band coefficient; secondly, obtaining an expression of a conditional likelihood ratio by using modified logarithmic Gaussian distribution and mixed exponential distribution according to corresponding different masks; thirdly, solving a priori ratio according to direction information of non-subsampled Contourlet transformation obtained by a neighborhood directivity model; and finally, obtaining a reduction factor according to the likelihood ratio and the priori ratio to reduce each high-frequency sub-band coefficient, and reconstructing the coefficient after the reduced change to obtain an output image after reducing the speckle noises. The method has the advantages of effectively eliminating the speckle noises of the SAR image and having good marginal retentivity, and can be applied to reducing the speckle noises of SAR images with abundant marginal information and abundant details, particularly the SAR images containing airports, runways and roads.

Algorithm for automatic image dodging of unmanned aerial vehicle images using two-dimensional radiometric spatial attributes

Abstract: Unmanned aerial vehicle (UAV) remote sensing technology has come into wide use in recent years. The poor stability of the UAV platform, however, produces more inconsistencies in hue and illumination among UAV images than other more stable platforms. Image dodging is a process used to reduce these inconsistencies caused by different imaging conditions. We propose an algorithm for automatic image dodging of UAV images using two-dimensional radiometric spatial attributes. We use object-level image smoothing to smooth foreground objects in images and acquire an overall reference background image by relative radiometric correction. We apply the Contourlet transform to separate high- and low-frequency sections for every single image, and replace the low-frequency section with the low-frequency section extracted from the corresponding region in the overall reference background image. We apply the inverse Contourlet transform to reconstruct the final dodged images. In this process, a single image must be split into reasonable block sizes with overlaps due to large pixel size. Experimental mosaic results show that our proposed method reduces the uneven distribution of hue and illumination. Moreover, it effectively eliminates dark-bright interstrip effects caused by shadows and vignetting in UAV images while maximally protecting image texture information.

Contourlet domain image denoising using the alpha-stable distribution

Abstract: In this paper, a new contourlet-based method for denoising of images corrupted by additive white Gaussian noise is proposed. The alpha-stable distribution is used to model the contourlet coefficients of noise-free images. This model is then exploited to develop a Bayesian minimum mean absolute error estimator. A modified empirical characteristic function-based method is employed for estimating the parameters of the assumed alpha-stable prior. The performance of the proposed denoising method is evaluated by using standard noise-free images corrupted with simulated noise and compared with that of the other state-of-the-art methods. The results show that the proposed method provides values of the peak signal-to-noise ratio higher than that provided by some of the existing techniques along with superior visual quality images.

High-resolution remote sensing image registration method with control points distributed in adaptive manner

Abstract: The invention discloses a high-resolution remote sensing image registration method with control points distributed in an adaptive manner. By the use of the high-resolution remote sensing image registration method, a multi-scale J-image is introduced into image registration. A self-adaptive control points extracting method based on a partitioning strategy is provided to extract the control points of the multi-scale J-image image, thus the defect that the control points only sense specific directional high-frequency information is overcome. A self-adaptive control points extracting strategy is defined so as to limit control point distribution. The control points are subjected to multi-scale matching by NMI (normalized mutual information measure), allowing registration function smooth. Geometric correction is realized by adopting Delaunay triangle local transformation. In the following text, basic principle and key steps of the algorithm are introduced, and three remote-sensing image groups of different types are subjected to experiment and analysis and comparison experiments with various registration methods based on wavelet transform and NSCT (non-subsampled contourlet transform).