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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Papers
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TL;DR: In this article, the authors proposed a method for noisy image fusion in contourlet domain, where the fusion algorithm is combined with a denoising algorithm to reverse the effect of noise.
Abstract: Image fusion is a challenging area of research with a variety of applications. The process of image fusion collects information from different sources and combines them in a single composite image. The composite fused image can better describe the scene than any of the source images. In this paper, we have proposed a method for noisy image fusion in contourlet domain. The proposed method works equally well for fusion of noise free images. Contourlet transform is a multiscale, multidirectional transform with various aspect ratios. These properties make it more suitable for image fusion than other conventional transforms. In the proposed work, the fusion algorithm is combined with a denoising algorithm to reverse the effect of noise. In the proposed method, we have used a level dependent threshold that is based on standard deviation of contourlet coefficients, mean and median of the absolute contourlet coefficients. Experimental results demonstrate that the proposed method performs well in the prese...
17 citations
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07 Apr 2008TL;DR: It is shown that the accuracy of the contourlet transform features in such conditions is more than that of the wavelet transform, which is still applicable in many texture classification tasks.
Abstract: Contourlet transform is a new two-dimensional extension of the wavelet transforms using multiscale and directional filter banks. In this paper, the effectiveness of the features obtained from the contourlet transform is investigated and is compared with the wavelet transform features for image texture classification. We specially focused on image acquisition conditions that an image from one scene may be acquired with different illumination, scale, direction, distance and slope. It is shown that the accuracy of the contourlet transform features in such conditions is more than that of the wavelet transform. However, wavelet transform is still applicable in many texture classification tasks.
17 citations
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03 Jun 2011TL;DR: The Multiscale Directional Filter Bank (MDFB) improves the radial frequency resolution of the Contourlet Transform by introducing an additional decomposition in the high frequency band and reduces the computational complexity significantly by saving a directional decomposition because of the change in the order of decomposition.
Abstract: This paper presents a novel approach for Gaussian noise removal using Multiscale Filter Banks for the Contourlet Transform. The Multiscale Directional Filter Bank (MDFB) improves the radial frequency resolution of the Contourlet Transform by introducing an additional decomposition in the high frequency band. This reduces the computational complexity significantly by saving a directional decomposition because of the change in the order of decomposition. Scaling is performed by a low pass filtering based splitting and the scale decomposition is done by the Directional Filter Bank. Perfect reconstruction is possible for the scale decomposition regardless of the choice of the low pass filter. MDFB outperforms the conventional Wavelet and Contourlet transform methods for Gaussian noise removal. Denoising performance of this proposed method is compared with Wavelet and Contourlet based denoising schemes with state of art threshold methods.
17 citations
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22 Mar 2013
TL;DR: This paper represents a approach to implement image fusion algorithm ie LAPLACIAN PYRAMID that implements a pattern selective approach to image fusion and reconstructs the fused image from the fused pyramid.
Abstract: This paper represents a approach to implement image fusion algorithm ie LAPLACIAN PYRAMID. In this technique implements a pattern selective approach to image fusion. The basic idea is to perform a pyramid decomposition on each source image and finally reconstruct the fused image by performing an inverse pyramid transform. It offers benefits like resolution, S/N ratio and pixel size. The aim of image fusion, apart from reducing the amount of data, is to create new images that are more suitable for the purposes of human/machine perception, and for further image-processing tasks such as segmentation, object detection or target recognition in applications such as remote sensing and medical imaging Based on this technique finally it reconstructs the fused image from the fused pyramid.
17 citations
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TL;DR: With the nonsubsampled contourlet transform (NSCT), a novel region-segmentation-based fusion algorithm for infrared (IR) and visible images is presented and Experimental results show that the proposed algorithm outperforms the pixel-based methods, including the traditional wavelet- based method and NSCT-based method.
Abstract: With the nonsubsampled contourlet transform (NSCT), a novel region-segmentation-based fusion algorithm for infrared (IR) and visible images is presented. The IR image is segmented according to the physical features of the target. The source images are decomposed by the NSCT, and then, different fusion rules for the target regions and the background regions are employed to merge the NSCT coefficients respectively. Finally, the fused image is obtained by applying the inverse NSCT. Experimental results show that the proposed algorithm outperforms the pixel-based methods, including the traditional wavelet-based method and NSCT-based method.
17 citations