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A Robust Watermarking Scheme for Information Hiding

A Robust Watermarking Scheme for Information Hiding

引用

International conference on Advanced Computing and Communications (ADCOM)

作者： K. Ramani E. V. Prasad S. Varadarajan A. Subramanyam SVN Engineering College CSED Tirupati India J.N.T.U. College of Engineering Kakinada India SVU College of Engineering EED Tirupati India AITS CSED Rajampet Andhra Pradesh India

wavelet techniques can be successfully applied in various signal and image processing applications, namely in image de-noising, segmentation, classification, motion estimation and copy right protection. The proliferation of digitized media due to the rapid growth of networked multimedia systems has created an urgent need for copyright enforcement technologies that can protect copyright ownership of multimedia objects. Digital image watermarking is one such technology that has been developed to protect digital images from illegal manipulations. In this paper, a robust and imperceptible watermarking scheme for copy right protection is proposed. The method is based on decomposing an image using the discrete wavelet transform, and then embedding locations are generated from the low frequency sub-band by using secrete sort to improve the embedding intensity. From the experimental results, the proposed scheme provides not only good quality, but also good robustness against external attacks, such as rotation, compression, cropping, noise and scaling.

关键词： Robustness Watermarking Protection Multimedia systems Digital images Discrete wavelet transforms signal processing image processing image denoising image segmentation

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A new combination of 1D and 2D filter banks for effective multiresolution image representation

A new combination of 1D and 2D filter banks for effective mu...

引用

IEEE International conference on image processing

作者： Yuichi Tanaka Masaaki Ikehara Truong Q. Nguyen Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA Department of Electronics and Electrical Engineering Keio University Yokohama Kanagawa Japan

In this paper, an effective multiresolution image representation using the combination of 2D quincunx filter bank (FB) and directional wavelet transform (WT) is presented. The proposed method yields simple implementation and low calculation costs compared to the other 1D and 2D FB combinations or adaptive directional WTs. Furthermore, it is a nonredundant transform and realizes quad-tree like multiresolution representation. In applications on nonlinear approximation and image coding, the proposed filter bank shows visual quality improvements and has higher PSNR.

关键词： Filter bank image resolution image representation image coding wavelet transforms Frequency signal resolution image reconstruction Laplace equations Computational efficiency

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EM-based estimation of spatially variant correlated image noise

EM-based estimation of spatially variant correlated image no...

引用

IEEE International conference on image processing

作者： Bart Goossens Aleksandra Pizurica Wilfried Philips TELIN-IPI-IBBT Ghent University Ghent Belgium

In image denoising applications, noise is often correlated and the noise energy and correlation structure may even vary with the position in the image. Existing noise reduction and estimation methods are usually designed for stationary white Gaussian noise and generally work less efficient in this case because of the noise model mismatch. In this paper, we propose an EM algorithm for the estimation of spatially variant (nonstationary) correlated image noise in the wavelet domain. In particular, we study additive white Gaussian noise filtered by a space-variant linear filter. This general noise model is applicable to a wide variety of practical situations, including noise in Computed Tomography (CT). Results demonstrate the effectiveness of the proposed solution and its robustness to signal structures.

关键词： Gaussian noise wavelet domain Noise reduction Nonlinear filters Computed tomography image denoising Design methodology Additive white noise Noise robustness image restoration

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image deconvolution under poisson noise using sparse representations and proximal thresholding iteration

Image deconvolution under poisson noise using sparse represe...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： F. -X. Dupe M. J. Fadili J.-L. Starck GREYC UMR CNRS 6072 Caen France DAPNIA/SEDI-SAP CEA-Saclay Gif-sur-Yvette France

We propose an image deconvolution algorithm when the data is contaminated by Poisson noise. The image to restore is assumed to be sparsely represented in a dictionary of waveforms such as the wavelet or curvelet transform. Our key innovations are: First, we handle the Poisson noise properly by using the Anscombe variance stabilizing transform leading to a non-linear degradation equation with additive Gaussian noise. Second, the deconvolution problem is formulated as the minimization of a convex functional with a data-fidelity term reflecting the noise properties, and a non-smooth sparsity-promoting penalties over the image representation coefficients (e.g. l 1 -norm). Third, a fast iterative backward-forward splitting algorithm is proposed to solve the minimization problem. We derive existence and uniqueness conditions of the solution, and establish convergence of the iterative algorithm. Experimental results are carried out to show the striking benefits gained from taking into account the Poisson statistics of the noise. These results also suggest that using sparse-domain regularization may be tractable in many deconvolution applications, e.g. astronomy or microscopy.

关键词： Deconvolution Additive noise Gaussian noise Iterative algorithms image restoration Dictionaries wavelet transforms Technological innovation Degradation Nonlinear equations

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Multiresolution eigenspace and fisherspace face recognition

Multiresolution eigenspace and fisherspace face recognition

引用

IEEE signal processing and Communications applications (SIU)

作者： Alaa Eleyan Hasan Demirel Elektrik ve Elektronik Muhendisligi Bolumu Dogu Akdeniz Universitesi Gazimagusa KKTC Turkey

The paper introduces a multiresolution face recognition system using features extracted from eigen and fisher spaces. Discrete wavelet transform has been used to generate images at varying resolutions. Two methods are proposed to combine the features extracted from a set of face images with varying resolution. The first method is called the multiresolution feature concatenation (MFC), where we use principal component analysis (PCA) and linear discriminant analysis (LDA) as a dimensionality reduction process on each subband. Then the resulting projection coefficients of each subband are concatenated to perform classification. The second method is called the multiresolution majority voting (MMV), where the classification are done separately on each subband and then the majority voting is applied for making decision. The results obtained from both of the methods show promising results and MMV approach outperforms the MFC approach. Moreover, the two methods outperform the conventional PCA and LDA approaches respectively approach.

关键词： Principal component analysis image resolution Face recognition Feature extraction Discrete wavelet transforms Face Humans

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Robust EZW image transmission scheme using distributed-alamouti codes in relay networks

Robust EZW image transmission scheme using distributed-alamo...

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International conference on signal processing and Communication Systems, ICSPCS

作者： Trung Q. Duong Hans-Jurgen Zepernick Radio Communications Group Blekinge Institute of Technology Ronneby Sweden

A robust image transmission scheme deploying distributed-Alamouti space-time code (STC) over wireless relay networks is proposed. Specifically, we propose a cross-layer design combining application, networking, and physical layer to enhance the quality of multimedia data over error-prone wireless channels. At the application layer, a coded image sequence based on the wavelet transform is divided into two priorities. The higher priority contains the most significant information of image and the lower priority conveys less important information. At the physical layer, two pre-assigned relays cooperate to form a distributed-Alamouti code which help the source to transmit the low priority sequence of the image over error-prone wireless channels. At the networking layer, among the whole relay nodes in the networks we select two relay nodes which provides the maximum signal-to-noise ratio to convey the higher priority. With this strategy, we implicitly achieve unequal error protection for image transmission without controlling power or varying modulation and coding at the transmitter at the expense of increasing network's overhead (i.e. feedback information). The selection of relays needs limited feedback, hence, enabling our proposed scheme feasible in practical applications. Analytical and simulation results are provided to verify our proposal. It has been shown that the image quality is remarkably enhanced in terms of objective quality using the proposed scheme compared to the conventional transmission schemes.

关键词： Robustness image communication Relays Protective relaying Physical layer Feedback Space time codes Cross layer design image sequences wavelet transforms

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Segmentation of hyperspectral images using fuzzy approaches

Segmentation of hyperspectral images using fuzzy approaches

引用

IEEE signal processing and Communications applications (SIU)

作者： Gokhan Bilgin Sarp Erturk Tulay Yildirim Elektronik ve Haberleme Muhendisligi Bolumu Yildiz Teknik Universitesi Turkey Elektronik ve Haberleme Muhendisligi Bolumu Kocaeli Universitesi Turkey

In this paper fuzzy clustering algorithms are utilized for the segmentation of hyperspectral images. For this purpose fuzzy c-means and an extended version of this algorithm, namely the fuzzy Gustafson-Kessel algorithms are used. Because of the high dimensionality in hyperspectral images, the data dimension is reduced using the Discrete wavelet Transform. The advantage of using fuzzy approaches for the segmentation is that for every pixel fuzzy membership degrees can be obtained. Hereby, a novel method which includes the utilization of spatial information is developed for segmentation with increased accuracy. The method is called `within kernel phase correlation'. Furthermore, it is shown that by two- and three-dimensional Gaussian filtering of the fuzzy membership cube the accuracy can be increased.

关键词： Kernel Hyperspectral imaging Hyperspectral sensors Remote sensing image segmentation Clustering algorithms Feature extraction

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Inter-modal selective 3D coding of PET-CT datasets

Inter-modal selective 3D coding of PET-CT datasets

引用

European signal processing conference (EUSIPCO)

作者： Alberto Signoroni Stefano Masneri Riccardo Leonardi Isabella Castiglioni DEA - University of Brescia Brescia Italy IBFM-CNR Segrate Mi Italy

ISBN: (纸本)9782839904506

In this work we introduce a new selective coding approach suitable for co-registered multi-modal medical images and we apply it to large PET-CT volumes. Salience information guiding a space variant reconstruction quality of the anatomical volume (CT) is generated through an automatic analysis of the functional volume (PET). This allows a versatile multiple volume-of-interest coding with arbitrary 3D-shape and scaling-factors and without the need of side-information to be transmitted. The proposed solutions are suitable for critical applications where high and optimized compression ratio, minimization of human intervention and full diagnostic quality preservation are all required.

关键词： Computed tomography Positron emission tomography Encoding image coding Transform coding Three-dimensional displays wavelet transforms

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A Statistical Speckle Suppression Algorithm for Underwater Laser image Based on Nonsubsampled Contourlet Transform

A Statistical Speckle Suppression Algorithm for Underwater L...

引用

International conference on Intelligent Systems Design and applications (ISDA)

作者： Liu Yang Baolong Guo Wei Ni Institute of Intelligent Control and Image Engineering Xidian University Xi'an China

Underwater laser image is seriously contaminated by speckle noise which is due to the coherent nature of the scattering phenomenon. In this paper, the authors propose an adaptive speckle suppression algorithm via the novel nonsubsampled contourlet transform. The statistical model for speckle noise is first analyzed to obtain a simple and tractable solution in a closed analytical form. Gaussian distribution for speckle noise and a general Gaussian distribution are adopted to model the statistics of contourlet coefficients in logarithmically transformed laser images. Then based on the maximizing the a posteriori estimation with the assumption that speckle noise is spatially correlated within a small window, we utilize a locally adaptive Bayesian processor whose parameters are obtained from the neighboring coefficients in highpass subbands. Experimental results show that comparing with classical wavelet method, the proposed algorithm shows a superior performance in suppressing the speckle noise and retaining geometrical structures of the image.

关键词： Speckle Laser noise Laser modes signal processing algorithms Gaussian distribution image edge detection Scattering Statistical distributions Bayesian methods image analysis

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An Efficient Algorithm for Compressed MR Imaging using Total Variation and wavelets

An Efficient Algorithm for Compressed MR Imaging using Total...

引用

26th IEEE conference on Computer Vision and Pattern Recognition (CVPR 2008), vol.2

作者： Shiqian Ma Wotao Yin Yin Zhang Amit Chakraborty Department of IEOR Columbia University New York NY USA Department of CAAM Rice University Houston TX USA Integrated Data Systems Siemens AG Corporate Research and Development Princeton NJ USA

Compressed sensing, an emerging multidisciplinary field involving mathematics, probability, optimization, and signal processing, focuses on reconstructing an unknown signal from a very limited number of samples. Because information such as boundaries of organs is very sparse in most MR images, compressed sensing makes it possible to reconstruct the same MR image from a very limited set of measurements significantly reducing the MRI scan duration. In order to do that however, one has to solve the difficult problem of minimizing nonsmooth functions on large data sets. To handle this, we propose an efficient algorithm that jointly minimizes the l{sub}1 norm, total variation, and a least squares measure, one of the most powerful models for compressive MR imaging. Our algorithm is based upon an iterative operator-splitting framework. The calculations are accelerated by continuation and takes advantage of fast wavelet and Fourier transforms enabling our code to process MR images from actual real life applications. We show that faithful MR images can be reconstructed from a subset that represents a mere 20 percent of the complete set of measurements.

关键词： signal processing algorithms image reconstruction image coding Compressed sensing Iterative algorithms Mathematics signal processing Magnetic resonance imaging Least squares methods Acceleration

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