Wavelet-based Contourlet Research Articles

Radar target recognition using contourlet packet transform and neural network approach

Contourlet is a “true” two-dimensional transform that captures the intrinsic geometrical structure and have been shown to be successful for many tasks in image processing. In this paper, a wavelet-based contourlet packet (WBCP) transform is investigated and an adaptive contourlet packet (ACP) transform based on genetic algorithm (GA) is proposed to extract the features of radar targets in synthetic aperture radar (SAR) images recognition. The features of the sampled targets are subsequently used to train a radical basis function neural network (RBFNN) that is then able to quickly and reliably recognize the objects. In comparison with WBCP, our proposed ACP has relatively low computational complexity and high recognition rate. Finally, we show some numerical experiments demonstrating the potential of this method for target recognition in SAR image processing.

Wavelet-based contourlet in quality evaluation of digital images

Feature extraction is probably the most important stage in image quality evaluation—effective features can well reflect the quality of digital images and vice versa. As a non-redundant sparse representation, contourlet transform can effectively reflect visual characteristics of images, and it can be employed to perceptually capture the difference between images. Motivated by this, this paper first proposes an objective reduced-reference image quality evaluation metric based on contourlet transform. Experiments demonstrate that this new objective metric achieves consistent image quality evaluation results with what gained by subjective evaluation.

基于WBCT和PCA的图像融合新方法

A novel image fusion algorithm based on wavelet-based contourlet transform (WBCT) and principal component analysis (PCA) is proposed. The PCA method is adopted for the low-frequency components. Using the proposed algorithm to choose the greater of the active measures, the region consistency test is performed for the high-frequency components. Experiments show that the proposed method works better in preserving the edge and texture information than wavelet transform method and Laplacian pyramid (LP) method do in image fusion. Four indicators for the fusion image are given to compare the proposed method with other methods.