due to damaging effects of speckle noise to information of radar images, reduction of these effects has been considered by many researchers. we discussed speckle reduction of radar images based on curvelet transformation. more specifically we discussed the speckle reduction with emphasizing on preservation of the edges by hard thresholding of curvelet transformation. in this algorithm, first mu...

The iris texture curve features play an important role in iris recognition. Although better performance in terms of recognition effectiveness can be attained using the recognition approach based on the wavelet transform, the iris curve singularity cannot be sparsely represented by wavelet coefficients. In view of the better approximation accuracy and sparse representation ability of the Curvele...

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 authors describe the Curvelet representation of image (object) with dominant angular subbands in Curvelet domain and analyse about the energy distribution for each subbands at different angles. Curvelet transform is localized not only in position (the spatial domain) and scale (the frequency domain), but also in orientation. Here energy of dominant orientations (angles) in a given...

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 describe an algorithm based on shrinkage in the curvelet domain to attenuate speckles in optical coherence tomography (OCT) images. The algorithm exploits the curvelet transform's sparse representation of edge discontinuities that are common in OCT images and its ability to map signals and noise into different areas in the curvelet domain. The speckle attenuation is controlled by a single pa...

This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study local structure in images. The permutation of Curvelet coefficients from original ...

The images usually bring different kinds of noises in process of receiving, coding and transmission. The curvelet transform is one kind of new multi-scale transform after 1999 that is based on wavelet transform, whose structural elements include the parameters of dimension and location, and orientation parameter more, which let curvelet transform has good orientation characteristic. Therefore, ...

The uniform discrete curvelet transform (UDCT) is a modified version of the curvelet transform based on the filter bank implementation in the frequency domain. In this project, the hidden Markov tree model, which captures the parent-children dependency, is used for the uniform discrete curvelet transform. Also, the image denoising results are shown as an application of the modeling.

In this paper we put forward an image fusion algorithm based on Wavelet Transform, Second Generation Curvelet Transform and Nonsubsampled Contourlet Transform. Here we analyze the characteristics of NSCT along with Wavelet and Curvelet Transform for simulation experiments on multi-focus images. Here it includes multiresolution analysis ability in wavelet transform, also has better direction abi...