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 ...

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An a...

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...

Multimodal medical image fusion is a powerful tool in clinical applications such as noninvasive diagnosis, image-guided radiotherapy, and treatment planning. In this paper, a novel nonsubsampled Contourlet transform (NSCT) based method for multimodal medical image fusion is presented, which is approximately shift invariant and can effectively suppress the pseudo-Gibbs phenomena. The source medi...

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...

All rights reserved by www.ijsrd.com 2296 Abstract--Image fusion is used to generate information from multiple images of the same scene to get a composite image that is more suitable for image-processing tasks i.e. Surveillance, Medical applications. In this paper the multisource medical images like MRI (Magnetic Resonance Imaging), CT (computed tomography) & PET (positron emission tomography) ...

The application of image restoration is not limited to the case of medical images especially in case of high resolution brain MRI images. The fractional Brownian motion noise present in these images affects certain important features which are needed for the proper diagnosis of brain diseases. Removal of fBm noise in these images is a kind of difficulty the researcher experiences. This paper in...

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...

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...

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...