Continuity along reflectors in seismic images is used via Curvelet representation to stabilize the convolution operator inversion. The Curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). Our iterative Curvelet-regularized deconvolution algorithm combine...

We present in this paper a new method for contrast enhancement based on the curvelet transform. The curvelet transform represents edges better than wavelets, and is therefore well-suited for multiscale edge enhancement. We compare this approach with enhancement based on the wavelet transform, and the Multiscale Retinex. In a range of examples, we use edge detection and segmentation, among other...

CT image De-noising is an important research topic both in image processing and biomedical engineering. Independent component analysis (ICA) is a statistical technique where the goal is to represent a set of random variables as a linear transformation of statistically independent component variables. The curvelet transform as a multiscale transform has directional parameters occurs at all scale...

There has been much research about using Gabor wavelet for face recognition. Other multiscale geometrical tools, such as curvelet and contourlet, have also been used for face recognition, thus it is interesting to know which method performs best, especially under illumination and expression changes. In this paper, we make a systematic comparison of wavelet, Gabor and curvelet for recognition, a...

Region based image retrieval has received significant attention from recent researches because it can provide local description of images, object based query, and semantic learning. In this paper, we apply curvelet transform to region based retrieval of color images. The curvelet transform has shown promising result in image de-noising, character recognition, and texture image retrieval. Howeve...

A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the grad...

The Digital Curvelet Transform provides near-optimal reconstruction of twice-continuously differentiable (C) curves. Previous implementations of the algorithm have not exploited newer technologies in modern processors, including the MMX and SSE instruction sets. Various optimization techniques were used on a form of the Digital Curvelet Transform resulting in the fastest Curvelet Transform curr...

In this work, a multi-scale feature point detection algorithm in CT slices based on discrete curvelet transform is presented. Discrete curvelet transformation is applied to input CT slices and the behavior of curvelet coefficients in all the scales are examined. The information in the fine and detail levels which contains the edge and singularity details are processed to extract the feature poi...

A digital image watermarking algorithm based on fast curvelet transform is proposed. Firstly, the carrier image is decomposed by fast curvelet transform, and, the watermarking image is scrambled by Arnold transform. Secondly, the binary watermarking image is embedded into the medium frequency coefficients according to the human visual characteristics and curvelet coefficients. Experiment result...

This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59% classification accuracy rate, which indicate ...