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...

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...