ShearLab 3D: Faithful Digital Shearlet Transforms based on Compactly Supported Shearlets

Wavelets and their associated transforms are highly efficient when approximating and analyzing one-dimensional signals. However, multivariate signals such as images or videos typically exhibit curvilinear singularities, which wavelets are provably deficient of sparsely approximating and also of analyzing in the sense of, for instance, detecting their direction. Shearlets are a directional representation system extending the wavelet framework, which overcomes those deficiencies. Similar to wavelets, shearlets allow a faithful implementation and fast associated transforms. In this paper, we will introduce a comprehensive carefully documented software package coined ShearLab 3D (www.ShearLab.org) and discuss its algorithmic details. This package provides MATLAB code for a novel faithful algorithmic realization of the 2D and 3D shearlet transform (and their inverses) associated with compactly supported universal shearlet systems incorporating the option of using CUDA. We will present extensive numerical experiments in 2D and 3D concerning denoising, inpainting, and feature extraction, comparing the performance of ShearLab 3D with similar transform-based algorithms such as curvelets, contourlets, or surfacelets. In the spirit of reproducible reseaerch, all scripts are accessible on www.ShearLab.org.Comment: There is another shearlet software package (http://www.mathematik.uni-kl.de/imagepro/members/haeuser/ffst/) by S. H\"auser and G. Steidl. We will include this in a revisio

Multisensor image fusion approach utilizing hybrid pre-enhancement and double nonsubsampled contourlet transform

A multisensor image fusion approach established on the hybrid-domain image enhancement and double nonsubsampled contourlet transform (NSCT) is proposed. The hybrid-domain pre-enhancement algorithm can promote the contrast of the visible color image. Different fusion rules are, respectively, selected and applied to obtain fusion results. The double NSCT framework is introduced to obtain better fusion performance than the general single NSCT framework. Experimental outcomes in fused images and performance results demonstrate that the presented approach is apparently more advantageous

Forensic Technique for Detection of Image Forgery

Todays digital image plays an important role in all areas such as baking, communication, business etc. Due to the availability of manipulation software it is very easy to manipulate the original image. The contents in an original image can be copy-paste to hide some information or to create tampering. The new area introduces to detect the forgery is an image forensic. In this paper proposes the new image forensic technique to detect the presence of forgery in the compressed images and in other format images. The proposed method is based on the no subsampled contoured transform (NSCT). The proposed method is made up of three parts as preprocessing, nsct transform and forgery detection. The proposed forensic method is flexible, multiscale, multidirectional, and image decomposition is shift invariant that can be efficiently implemented via the à trous algorithm. The proposed a design framework based on the mapping approach. This method allows for a fast implementation based on a lifting or ladder structure. The proposed method ensures that the frame elements are regular, symmetric, and the frame is close to a tight one. The NSCT compares with and dct method in this paper

Fusion of Infrared and Visible Images Based on Non-subsample Contourlet Transform

For the single spectrum image could not fully express the target feature information, this paper proposed a multispectral image fusion method based on non-subsample contourlet transform (NSCT). For the low frequency coefficients decomposed, fourth-order correlation coefficient is used to calculate the correlation between each low frequency coefficients, averaging fusion for the higher correlation coefficient, weight phase congruency fusion for the low correlation coefficient. For high frequency coefficients, Gaussian weight sum modified Laplace method is used for fusing, to retain more local structure details. Simulation results show that the method effectively retain the image structure information and more local details, and increase the image contrast

Spatial Images Feature Extraction Based on Bayesian Nonlocal Means Filter and Improved Contourlet Transform

Spatial images are inevitably mixed with different levels of noise and distortion. The contourlet transform can provide multidimensional sparse representations of images in a discrete domain. Because of its filter structure, the contourlet transform is not translation-invariant. In this paper, we use a nonsubsampled pyramid structure and a nonsubsampled directional filter to achieve multidimensional and translation-invariant image decomposition for spatial images. A nonsubsampled contourlet transform is used as the basis for an improved Bayesian nonlocal means (NLM) filter for different frequencies. The Bayesian model adds a sigma range in image a priori operations, which can be more effective in protecting image details. The NLM filter retains the image edge content and assigns greater weight to similarities for edge pixels. Experimental results both on standard images and spatial images confirm that the proposed algorithm yields significantly better performance than nonsubsampled wavelet transform, contourlet, and curvelet approaches

IMAGE SEPARATION BASED ON NONSUBSAMPLED CONTOURLET(NSCT)

ABSTRACT When an image or a document on a paper acquired through scannin

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain method

Adaptive filter and threshold for image denoising in new generation wavelet

In reality, the nature images have the noise values because of many reasons. These values make the quality of images to decrease. Wavelet transform is proposed for denoising and it gives the better results. But with curvelet transform, one of the new generations of wavelet, the quality of images continues to be improved. In this paper, my proposed method is to combine filter and threshold to calculate the denoising coefficients in curvelet domain. The result of proposed method is compared with other previous methods and shows an improvement