Contourlet

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

Differentiating Between Images Using Wavelet-Based Transforms: A Comparative Study

Abstract: We propose statistical image models for wavelet-based transforms, investigate their use, and compare their relative merits within the context of digital image forensics. We consider the problems of 1) differentiating computer graphics images from photographic images, 2) source camera and source scanner identification, and 3) source artist identification from digital painting samples. The features obtained from ridgelet and contourlet transform-based image models almost always perform better than the features obtained from wavelet-based image models for the problems at hand. We outline properties of efficient image representation, relate these properties to wavelet-based transforms, and discuss the experimental results in relation to the model properties.

Pan-sharpening via the contourlet transform

Abstract: The wavelet transform has been a popular choice for the spatial transformation in the pan-sharpening process. However, the wavelet transform do not represent the directional information efficiently. On the other hand, the contourlet transform, which also has a property of multiresolution decomposition similar to the wavelet, is known to provide efficient directional information and is also useful in capturing intrinsic geometrical structures of the objects. This property of contourlet transformation is very useful for images that contain geometric features. Principal component analysis (PCA) is generally used for the spectral transformation. In this paper, an alternative algorithm based on the merger of PCA-contourlet transform for pan-sharpening is presented. The efficiency of this method is tested by performing pan-sharpening of the high resolution (IKONOS and Quickbird) and the medium resolution (LandSat7 ETM+) datasets. The resulting pan-sharpened images are evaluated in terms of known global validation indexes. These indexes reveal that this method provides better fusion results than the PCA-wavelet approach.

Image denoising based on the symmetric normal inverse Gaussian model and non-subsampled contourlet transform

Abstract: In this study, an adaptive image denoising method is proposed based on the symmetric normal inverse Gaussian (SNIG) model and the non-subsampled contourlet transform (NSCT). In the framework of Bayesian maximum a posteriori estimation, the problem of denoising is reduced to a procedure of thresholding. A novel strategy is then proposed to determine the threshold that is not only adaptive to different directions and scales, but also able to take into considerations the scale-to-scale difference in the contribution of the NSCT coefficients to the noise. The experimental results in different kinds of sample images show that the authors' method can not only result in higher peak-signal-to-noise ratio values, but also have better visual effects in reduced processing artefacts and preserved edges.

Feature extraction based on contourlet transform and its application to surface inspection of metals

Abstract: Surface defects that affect the quality of metals are an important factor. Machine vision systems commonly perform surface inspection, and feature extraction of defects is essential. The rapidity and universality of the algorithm are two crucial issues in actual application. A new method of feature extraction based on contourlet transform and kernel locality preserving projections is proposed to extract sufficient and effective features from metal surface images. Image information at certain direction is important to recognition of defects, and contourlet transform is introduced for its flexible direction setting. Images of metal surfaces are decomposed into multiple directional subbands with contourlet transform. Then features of all subbands are extracted and combined into a high-dimensional feature vector, which is reduced to a low-dimensional feature vector by kernel locality preserving projections. The method is tested with a Brodatz database and two surface defect databases from industrial surface-inspection systems of continuous casting slabs and aluminum strips. Experimental results show that the proposed method performs better than the other three methods in accuracy and efficiency. The total classification rates of surface defects of continuous casting slabs and aluminum strips are up to 93.55% and 92.5%, respectively.

Compressed sensing MRI based on nonsubsampled contourlet transform

Abstract: How to reduce acquisition time is very important in magnetic resonance imaging (MRI). Compressed sensing MRI emerges recently to suppress the aliasing when undersampling k-space data is employed. However, typical sparse transform for compressed sensing MRI ever used is wavelet, which only captures limited directional information with decay rate M1. In this paper, we introduce contourlet into compressed sensing to obtain a sparse expansion for smooth contours with decay rate C(logM)3M2 and employ nonsubsampled contourlet to increase the redundancy of basis for magnetic resonance images. We propose compressed sensing MRI based on nonsubsampled contourlet transform (NSCT). Experimental results demonstrate that NSCT outperforms wavelet on suppressing the aliasing and improves the visual appearance of magnetic resonance images.