Topic
Contourlet
About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.
Papers published on a yearly basis
Papers
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10 Jul 2016TL;DR: This article explores and proposes a texture based classification of remotely sensed multispectral images using features derived from the wavelet, curvelet and contourlet transforms, and shows how class separability is defined in feature space.
Abstract: Multi-resolution analysis (MRA) has been successfully used in image processing with the recent emergence of applications to texture classification. Several studies have investigated the discriminating power of wavelet-based features in various applications such as image compression, image denoising, and classification of natural textures. Recently, the curvelet and contourlet transforms have emerged as new multi-resolution analysis tools to deal with non-linear singularities present in the image. This article explores and proposes a texture based classification of remotely sensed multispectral images using features derived from the wavelet, curvelet and contourlet transforms. These features characterize the textural properties of the images and are used to train the classifier to recognize each texture class. Using these MRA based feature descriptors class separability is defined in feature space. The results are compared with Grey Level Co-occurrence Matrix (GLCM) based statistical features.
12 citations
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TL;DR: A novel approach of getting high resolution image from a single low resolution image is discussed, which is capable to reconstruct an image with less edge artifacts and the validity of the proposed approach is proven through simulation on several images.
Abstract: Enhancing the quality of image is a continuous process in image processing related research activities. For some applications it becomes essential to have best quality of image such as in forensic department, where in order to retrieve maximum possible information, image has to be enlarged in terms of size, with higher resolution and other features associated with it. Such obtained high quality images have also a concern in satellite imaging, medical science, High Definition Television (HDTV), etc. In this paper a novel approach of getting high resolution image from a single low resolution image is discussed. The Non Sub-sampled Contourlet Transform (NSCT) based learning is used to learn the NSCT coefficients at the finer scale of the unknown high-resolution image from a dataset of high resolution images. The cost function consisting of a data fitting term and a Gabor prior term is optimized using an Iterative Back Projection (IBP). By making use of directional decomposition property of the NSCT and the Gabor filter bank with various orientations, the proposed method is capable to reconstruct an image with less edge artifacts. The validity of the proposed approach is proven through simulation on several images. RMS measures, PSNR measures and illustrations show the success of the proposed method.
12 citations
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03 Apr 2014TL;DR: In this article, a novel algorithm called Contourlet Transform is proposed to detect the blood vessels efficiently, which is the extension of wavelet transform used to enhance the retinal image then the image is utilized for the segmentation part.
Abstract: Retinal images play a vital role in most of the applications like ocular fundus operations and human recognition. Also, it is used to detect the diabetes in early stages by evaluating all the retinal blood vessels together. The detection of blood vessels from the retinal images is generally a slow process. In this paper, a novel algorithm called Contourlet Transform is proposed to detect the blood vessels efficiently. The proposed Contourlet Transform is the extension of wavelet transform used to enhance the retinal image then the image is utilized for the segmentation part. The existing curvelet transform has disadvantages that is directional specificity of the image is less owing to that the effectiveness is poor. The directionality features of the multistructure elements technique construct it as an effectual tool in edge detection. Therefore, morphology operators by means of multistructure elements are given to the enhanced image in order to locate the retinal image ridges. Later, morphological operators by reconstruction eradicate the ridges not related to the vessel tree as trying to protect the thin vessels that are unaffected. This approach uses multistructure elements in order to improve the performance of morphological operators by reconstruction. An improved Ostu thresholding method is combined with Strongly Connected Component Analysis (SCCA) which indicates the remained ridges pertaining to vessels. The experimental results show the proposed method obtains 96% accuracy in detection of blood vessels and is compared with other existing approaches.
12 citations
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TL;DR: A texture based change detection method that applies curvelet and contourlet transforms on polarimetric synthetic aperture radar (SAR) images with high change detection accuracy with better edge continuity and greater AUC is proposed.
12 citations
01 Jan 2007
TL;DR: Experiment on image de-noising shows that the two methods both pick up the image detail better and improve the peak signal-to-noise ratio.
Abstract: A new method for image de-noising which colligated the strong point of Contourlet transform and the magnitude of the detail of image in every scale and direction.This paper deals with the problem of snuffing out the Contourlet coefficients to exceed,and it also can solve the problem of taking no consideration of image details.Experiment on image de-noising shows that:compare to the wavelet threshold,Contourlet threshold and multi-scale threshold using Contourlet transform,the two methods both pick up the image detail better and improve the peak signal-to-noise ratio.
12 citations