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...

We present in this paper a new method for con­ trast 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 de­ tection and segmentation, among o...

In this article a new method is introduced for distinguishing roots and background based on their digital curvelet transform in minirhizotron images. In the proposed method, the nonlinear mapping is applied on sub-band curvelet components followed by boundary detection using energy optimization concept. The curvelet transform has the excellent capability in detecting roots with different orient...