In this paper, we propose a new feature extraction approach for face recognition based on Curvelet transform and local binary pattern operator. The motivation of this approach is based on two observations. One is that Curvelet transform is a new anisotropic multi-resolution analysis tool, which can effectively represent image edge discontinuities; the other is that local binary pattern operator...

The purpose of this study was to develop a methodology for assessing fruit quality objectively using texture analysis based on Curvelet Transform. Being a multiresolution approach, curvelets have the capability to examine fruit surface at low and high resolution to extract both global and local details about fruit surface. The fruit images were acquired using a CCD color camera and guava and le...

Natural images are typically a composition of cartoon and texture structures. One common task is to separate such an image into two single images, one containing the cartoon part and the other containing the texture part. Recently, a powerful class of algorithms using sparse approximation and l1 minimization has been introduced to resolve this problem, and numerous inspiring empirical results h...

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...

Thresholding of Curvelet Coefficients, for image denoising, drains out subtle signal component in noise subspace. In effect, it also produces ringing artifacts near edges. We found that the sensitivity phases — contrast to their magnitude reduces with higher level. Thus, we preserved phase coefficients below threshold at coarser scale and estimated corresponding by Joint Bilateral Filtering (JB...

Image retrieval is the important technique in image processing. The paper presents the robust object recognition using texture and directional feature extraction. The system proposes texture descriptors such as fast discrete Curvelet transform based entropy feature and local directional pattern. The category recognition is to classify an object into one of several predefined categories. The Fas...

A key challenge in the seismic imaging of reflectors using surface reflection data is the subsurface illumination produced by a given data set and for a given complexity of the background model (of wavespeeds). The imaging is described here by the generalized Radon transform. To address the illumination challenge and enable (accurate) local parameter estimation, we develop a method for partial ...

A nonlinear singularity-preserving solution to seismic image recovery with sparseness and continuity constraints is proposed. We observe that curvelets, as a directional frame expansion, lead to sparsity of seismic images and exhibit invariance under the normal operator of the linearized imaging problem. Based on this observation we derive a method for stable recovery of the migration amplitude...

Most No-Reference Image Quality Assessment (NR-IQA) metrics are designed for the quality assessment of images distorted by compression, noise and blurring. Few NR-IQA metrics exist for Contrast-Distorted Images (CDI).Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for ContrastDistorted Images (NR-IQACDI) are the state-of-the-art IQA algorithms for CDI. Room...

Currently, it appears that the best method for non-Gaussianity detection in the Cosmic Microwave Background (CMB) consists in calculating the kurtosis of the wavelet coefficients. We know that wavelet-kurtosis outperforms other methods such as the bispectrum, the genus, ridgelet-kurtosis and curvelet-kurtosis on an empirical basis, but relatively few studies have compared other transform-based ...