Curvelet transform is the new member of the evolving family of multiscale geometric transforms. It offers an effective solution to the problems associated with image denoising using wavelets. Finger prints possess the unique properties of distinctiveness and persistence. However, their image contrast is poor due to mixing of complex type of noise. In this paper an attempt has been made to prese...

The terms Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT) are used to denote efficient and fast algorithms to compute the Discrete Fourier Transform (DFT) and the Inverse Discrete Fourier Transform (IDFT) respectively. The FFT/IFFT is widely used in many digital signal processing applications and the efficient implementation of the FFT/IFFT is a topic of continuous research.

Efficient representation of images usually leads to improvements in storage efficiency, computational complexity and performance of image processing algorithms. Efficient representation of images can be achieved by transforms. However, conventional transforms such as Fourier transform and wavelet transform suffer from discontinuities such as edges in images. To address this problem, we propose ...

The speckle degrades quality of the image and makes interpretations, segmentation of objects harder. In this paper, we present a method for object detection of speckle image base on curvelet transform. The approximate properties and the high directional sensitivity of the curvelet transform make the new method for object detection of speckle image. We construct a method segmentation that provid...

Fast Fourier Transform (FFT) is an efficient algorithm to compute the Discrete Fourier Transform (DFT) and its inverse. In this paper, we pay special attention to the description of complex-data FFT. We analyze two common descriptions of FFT and propose a new presentation. Our heuristic description is helpful for students and programmers to grasp the algorithm entirely and deeply.

A divide and conquer algorithm is presented for computing arbitrary multi-dimensional discrete Fourier transforms. In contrast to standard approaches such as the row-column algorithm, this algorithm allows an arbitrary decomposition, based solely on the size of the transform independent of the dimension of the transform. Only minor modifications are required to compute transforms with different...

Let Fq be the finite field with q elements and let ! be a primitive n-th root of unity in an extension eld Fqd of Fq. Given a polynomial P 2 Fq[x] of degree less than n, we will show that its discrete Fourier transform (P (1); P (!); :::; P (!n¡1)) 2Fqd n can be computed essentially d times faster than the discrete Fourier transform of a polynomial Q 2 Fqd[x] of degree less than n, in many case...

This paper presents a study on the performance of transformed domain features in Devnagari digit recognition. In this research the recognition performance is measured from features obtained in direct pixel value, Fourier Transform, Discrete Cosine Transform, Gaussian Pyramid, Laplacian Pyramid, Wavelet Transform and Curvelet Transform using classification schemes: Feed Forward, Function Fitting...

Spatial images are inevitably mixed with different levels of noise and distortion. The contourlet transform can provide multidimensional sparse representations of images in a discrete domain. Because of its filter structure, the contourlet transform is not translation-invariant. In this paper, we use a nonsubsampled pyramid structure and a nonsubsampled directional filter to achieve multidimens...

We compare several algorithms for computing the discrete Fourier transform of n numbers. The number of “operations” of the original Cooley-Tukey algorithm is approximately 2n A(n), where A(n) is the sum of the prime divisors of n. We show that the average number of operations satisfies (l/x)Z,,,2n A(n) (n2/9)(x2/log x). The average is not a good indication of the number of operations. For examp...