A new image denoising method based on curvelet transform is proposed. The limitations of commonly used separable extensions of one-dimensional transforms, such as the Fourier and wavelet transforms, in capturing the geometry of image edges are well known. Here, we pursue "true" two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information. Deno...

In this project, a modified version of the curvelet transform is proposed for image denoising. We introduced the complex Gaussian scale mixture (CGSM) for modeling the distribution of complex curvelet coefficients. The statistical model is then used to obtain the denoised coefficients from the noisy image decomposition by Bayes least squares estimator. Performance of the denoised images using t...

Image Fusion is a data fusion technique which combines information of the two images which has varied information to form a new single image. The objective is to fuse an MR image and CT image of the same organ to obtain a single image containing as much information as possible. In this paper Wavelet Transform and Fast Curvelet Transform are highlighted to perform the image fusion of MR image an...

In this paper, an efficient image deblurring algorithm is proposed. This algorithm restores the blurred image by incorporating a curvelet-based empirical Wiener filter with a spatial-based joint non-local means filter. Curvelets provide a multidirectional and multiscale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditiona...

We propose a robust interpolation scheme for aliased regularly sampled seismic data that uses the curvelet transform. In a first pass, the curvelet transform is used to compute the curvelet coefficients of the aliased seismic data. The aforementioned coefficients are divided into two groups of scales: alias-free and alias-contaminated scales. The alias-free curvelet coefficients are upscaled to...

In this paper, an improved method based on Curvelet Transform using different window functions is presented for the speech enhancement. The window function is used for preprocessing of speech signals. In this method, instead of using two-dimensional (2-D) discrete Fourier Transform, Curvelet transform is employed with spectral magnitude subtraction method. General terms Spectral Substraction Me...

Edge detection is an important assignment in image processing, as it is used as a primary tool for pattern recognition, image segmentation and scene analysis. Simply put, an edge detector is a high-pass filter that can be applied for extracting the edge points within an image. Edge detection in the spatial domain is accomplished through convolution with a set of directional derivative masks in ...

We describe approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform and the curvelet transform. Our implementations offer exact reconstruction, stability against perturbations, ease of implementation, and low computational complexity. A central tool is Fourier-domain computation of an approximate digital Radon transform. We introduce a very simple ...

The emergence of multimedia technology and the rapid growth in the number and type of multimedia assets controlled by several entities, yet because the increasing range of image and video documents showing on the Internet, have attracted vital analysis efforts in providing tools for effective retrieval and management of visual data. So the need for image retrieval system arose. Out of many exis...

This paper describes a comparative analysis of recognition accuracy using feature extraction algorithm. A feature extraction algorithm is introduced for face recognition, Principle Component Analysis (PCA),Linear Discriminant Analysis(LDA) , Independent Component Analysis(ICA) and Nonnegative matrix factorization (NMF) based on curvelet transform. Mostly recognition system is capable to perform...