Contourlet

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

Weighted image fusion using cross bilateral filter and non-subsampled contourlet transform

Abstract: This paper provides a fusion technique for multi-focus imaging using cross bilateral filter and non-subsampled contourlet transform. The snapshots are decomposed into distinct approximation and detail components. The original image and approximation component is passed through cross bilateral filter to obtain approximation weight map. Whereas, the detail components are combined using weighted average to obtain detail weight map. The weights are combined together and used with original images to obtain the resultant fused image. Visual and quantitative analysis shows the significance of proposed scheme in comparison to state of art fusion schemes.

An edge detection scheme based on least squares support vector machine in a contourlet HMT domain

Abstract: We detected edges using least squares support vector machine classification in Contourlet Hidden Markov Tree Model.The comparison of the denoising and detecting results obtained with our scheme, and with the best state-of-the-art denoising and detecting edges techniques.Results were carried out on images corrupted with both Gaussian noise and an impulsive noise. In this paper, we have presented a new and effective edge detection scheme based on least squares support vector machine (LS-SVM) classification in a contourlet Hidden Markov Tree Model (contourlet HMT). First, the input noisy image is decomposed into coarser and finer coefficients using a contourlet HMT transform to derive an efficient multiscale and multidirectional image representation. Second, the feature vector is performed through spatial regularity in a contourlet HMT domain, and the coarser coefficients classified using LS-SVM classifier into two classes: noise coefficients and edge coefficients. Next, all noisy contourlet HMT coefficients are well denoised by the BayesShrink method.Finally, the denoised coefficients and edge coefficients are fused using the weighted average rule, and the inverse contourlet HMT is applied to obtain the edge image.Experimental results demonstrate that our scheme can attain improved performance over state-of-the-art edge detection approaches, both qualitatively and quantitatively. Tests were performed on several images from the Berkeley dataset corrupted with Gaussian noise and on other images such as a cameraman, pepper and medical images. The results illustrate that the performance of the proposed scheme is stable.

Mammograms Enhancement and Denoising Using Generalized Gaussian Mixture Model in Nonsubsampled Contourlet Transform

Abstract: In this paper, a novel algorithm for mammographic image s enhancement and denoising based on Multiscale Geometric Analysis (MGA) is proposed. Firstly mammograms are decomposed into different scales and directional subbands using Nonsubsampled Contourlet Transform (NSCT). After modeling the coefficients of each directional subbands using Generalized Gaussian Mixture Model (GGMM) according to the statistical property, they are categorized into strong edges, weak edges and noise by Bayes ian classifier. To enhance the suspicious lesion and suppress the noise, a nonlinear mapping function is designed to adjust the coefficients adaptively so as to obtain a good enhancement result with significant features. Finally, the resulted mammographic images are obtained by reconstructing with the modified coefficients using NSCT. Experimental results illustrate that the proposed approach is practicable and robustness, which outperforms the spatial filters and other methods based on wavelets in terms of mass and microcalcification denoising and enhancement.

Comparative study of imaging transforms on diabetic retinopathy images

Abstract: Retinal images are important in detection of ocular diseases. The retinal images have been evaluated and it has been used by doctors for the identification of retinal diseases like Diabetic Retinopathy and Hypertension. These images have low dynamic range and grey level contrast range. There are many alternative image enhancement techniques used for particular applications so that better diagnostic results can be obtained. This paper throws light on the uses of new imaging transform techniques for enhancement of retinal images like wavelet transform, curvelet transform and contourlet transform. The existing algorithms have been studied and the advantages and drawbacks of those algorithms are analysed and this paper gives clear picture of comparison between three imaging transforms. After experimental analysis contourlet transform gave best results for detection of edges compared to other two techniques. This method has been performed by calculating the Peak to signal noise ratio from datasets such as Drive and from medical institutions.