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 ...

In this paper, a denoising and binarization scheme of document images corrupted by white Gaussian noise and Impulse noise is presented using Curvelet Transform. The ability of sparse representation and edge preservation of Curvelet transform is utilized. Impulse noise gets added during document scanning or after binarization of scanned document images. White Gaussian noise corrupts the document...

Image-object extraction is one of the most important parts in the image processing. Object extraction is the technique of extracting objects from the pre-processed image in such a way that within – class similarity is maximized and between – class similarity is minimized. In this paper, a new method of extracting objects from grey scale static images using Fast Discrete Curvelet Transform (FDCT...

Curvelet transform is a multidirectional multiscale transform that enables sparse representations for signals. Curvelet-based feature extraction for Synthetic Aperture Radar (SAR) naturally enables utilizing spatial locality; the use of curvelet-based feature extraction is a novel method for SAR clustering. The implemented method is based on curvelet subband Gaussian distribution parameter esti...

The images usually bring different kinds of noise in the process of receiving, coding and transmission. In this paper the Curvelet transform is used for de-noising of image. Two digital implementations of the Curvelet transform (a multiscale transform) viz the Unequally Spaced Fast Fourier Transform (USFFT) and the Wrapping Algorithm are used to de-noise images degraded by different types of no...

Curvelet transformation proposed in 1999, on the basis of wavelet transformation, is a new multi-scale geometric analysis method. Besides scale parameter and location parameter, it adds an azimuth parameter in structure elements compared with wavelet transformation, which makes the Curvelet transformation express anisotropic singular boundary of lines or curves. So, Curvelet transformation is b...