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Piecewise-linear subband coding scheme for fast image decomposition

MULTIMEDIA TOOLS AND applications 2016年第17期75卷 10649-10666页

作者： Slusarczyk, Przemyslaw Baran, Remigiusz Jan Kochanowski Univ Inst Phys Dept Comp Sci Swietokrzyska 15 PL-25406 Kielce Poland Kielce Univ Technol Fac Elect Engn Automat & Comp Sci Al 1000 Lecia PP 7 PL-25314 Kielce Poland

Transform coding methods play a very important role in the development of diverse multimedia technologies. The discrete cosine and wavelet transforms are frequently used in the field of image processing and set up a basis for contemporary image compression standards (JPEG, MPEG). In this paper a novel two-channel piecewise-linear subband coding scheme (PL-SBC) is introduced. Its most important advantage are exceptionally fast and easy for implementation computational algorithms combined with good reconstruction quality. The proposed PLSBC system is based on the dual filter bank consisting of a single filter at the decomposition stage and a single one at the reconstruction stage. For the presented PL-SBC scheme, the conditions of perfect reconstruction and the piecewise-linear approximation filter bank have been defined. The compression properties of a new subband coding scheme have been analysed for different categories of images and in comparison with well-known signal transforms, such as cosine, piecewise-linear, piecewise-constant and wavelet transforms. The research includes a comparison of the reconstruction error both in objective as well as subjective approach.

关键词： Subband image coding Piecewise-linear approximation Linear transforms image compression

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image Fusion Models and Techniques at pixel level 2

Image Fusion Models and Techniques at pixel level

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2nd International Conference on image processing, applications and Systems (IPAS)

作者： Ben Salem, Yosra Hamrouni, Kamel Solaiman, Basel Natl Engn Sch Tunis ENIT Signal Image & Informat Proc Lab Tunis Tunisia TELECOM Bretagne Image & Informat Proc Dept Brest France

ISBN: (纸本)9781509016457

Information fusion consists in combining information in order to maximize the relevant information and reduce the redundancy. It is widely used in many fields, especially in image processing, for analyzing situations. Since the first use of the information fusion concept, many approaches have been introduced to define a processing model to merge information. Three basic approaches are used in information fusion: the JDL Model which is the first one used, the Intelligence Cycle Model and the DFD (Data - Features - Decision) Model. According to the field of application and the type of the information manipulated, the processing model is different. In image processing, various techniques and methods are used to perform image fusion. Many techniques are most used in research studies: PCA (Principal Component Analyses), wavelet transform,. In this paper we present a general overview of the basic models and techniques used in image fusion.

关键词： Information Fusion images Fusion JDL Fusion models

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wavelet Based New Hybrid image Denoising

Wavelet Based New Hybrid Image Denoising

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International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT)

作者： Easwara, M. Meenavathi, M. B. CBIT Dept E&CE Kolar India BIT Dept E&IE Bengaluru India

ISBN: (纸本)9781509046973

In the modern age, the transmission of digital images is one of the major processes of communication system. Meanwhile, images are often corrupted by the noise. In many image processing applications the reconstruction of high quality image is an essential fact. To achieve this, we proposed a wavelet based hybrid image denoising. In which, after wavelet decomposition of a noisy image both the approximation and the detail coefficients are incorporated into modification. Approximation coefficients are processed by the Cloud Model (CM) and estimated by the fuzzy mean without omitting the contribution of an individual. The detail parts undergo sub band based adaptive modified firm thresholding. Finally, the image is treated with an Adaptive Wiener filter, to further improve its visual quality. Evaluation indicates that our proposed algorithm outperforms over wavelet based Weighted High Frequency Kernel (WHFC) and median methods in terms of Peak signal to Noise Ratio (PSNR).

关键词： Adaptive hybrid fuzzy mean coefficients

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A New Method For Extraction of image's Features: Complex Discrete Ripplet-II Transform 24

A New Method For Extraction of Image's Features: Complex Dis...

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24th signal processing and Communication Application Conference (SIU)

作者： Yasar, Huseyin Ceylan, Murat TC Sagl Bakanligi Ankara Turkey KTO Karatay Univ Muhendislik Elekt Elekt Muhendisligi Bolumu Konya Turkey

ISBN: (纸本)9781509016792

wavelet transform extracts the features of a signal and image via shifting and weighting methods. This transform has either advantages or disadvantages on image processing applications. One of important disadvantage of wavelet transform is limited orientation problem. This problem has been solved by different orientation with ridgelet transform. Ripplet-II transform is defined by recently generalising of the ridgelet transform by adding parameter degree (d). Complex discrete form of ripplet-II transform defined by this study and added to the literature. Also, complex discrete Ripplet-II transform was tested on medical image classification application. For this, the most common benign lesions in liver MR are classified as cyst and hemangioma using complex discrete Ripplet-II transform and ANN. Obtained results shown that, classification success of complex discrete Ripplet-II transform with complexvalued coefficients is higher than real-valued coefficients.

关键词： ridgelet transform ripplet-II transform complex discrete ripplet-II transform cyst hemangioma liver classification

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Enhancement of Latent Fingerprints on Banknotes Using Monogenic wavelets 11

Enhancement of Latent Fingerprints on Banknotes Using Monoge...

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11th International Conference on signal processing and Communications (SPCOM)

作者： Birajadar, Parmeshwar Patidar, Varun Shirvalkar, Pushkaraj Gupta, Shubham Gadre, Vikram ITT Bombay Dept Elect Engn Bombay 400076 Maharashtra India

ISBN: (纸本)9781509017461

Latent fingerprints on banknotes provide valuable information in forensic applications to identify suspects of robbery cases and counterfeiters. The image quality of latent fingerprints is poor due to the complex background of banknotes compared to live-scan images acquired in a controlled environment. An effective orientation field estimation algorithm is necessary for enhancing poor quality latents in the presence of background noise for reliable extraction of minutia features. There have been attempts in the literature to characterize a fingerprint using an AM-FM model. Motivated by the ability of the monogenic signal representation to demodulate such a signal to extract its features, this paper proposes a novel algorithm of latent fingerprint multiresolution orientation estimation with a modified Simoncelli isotropic wavelet design and by an appropriate use of the multiresolution orientation field. Experimental results on synthetic noisy images and real latents from our created latent fingerprint database on banknotes show that the enhanced image quality by using the proposed algorithm is better than the other existing methods for estimating the fingerprint orientation.

关键词： image quality

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Non-stationary signal feature characterization using adaptive dictionaries and non-negative matrix factorization

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signal image AND VIDEO processing 2016年第6期10卷 1025-1032页

作者： Shokrollahi, Mehrnaz Krishnan, Sridhar Ryerson Univ Dept Elect & Comp Engn 350 Victoria St Toronto ON M5B 2K3 Canada

Our objective is to introduce a novel method of performing non-stationary signal analysis by means of enhanced training performance, suitable learning, and proper training dictionary elements in sparse representation techniques for real biomedical signal applications. Non-stationary signal characteristics pose severe challenges in terms of analysis and extraction of discriminant features. In addition, due to complexity of biomedical signals, the need for feature extraction algorithms that can localize to events of interest increases. To fulfil this objectives, we propose to use dictionary learning algorithms based on non-negative matrix factorization. This allows us to train the dictionary elements that lead to more robust classification performances. The proposed algorithm uses a time-frequency decomposition based on wavelet transform for non-stationary 1D biomedical signals. In this manuscript we aim to exploit non-stationary signal analysis through dictionary learning and study the discriminant features of these signals by means of sparse representation to design a robust algorithm in addition to higher classification performance.

关键词： Dictionary learning Sparse representation Measure of sparsity K-non-negative matrix factorization K-singular value decomposition EMG

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Exploring the Impact of wavelet-based Denoising in the Classification of Remote Sensing Hyperspectral images 22

Exploring the Impact of Wavelet-based Denoising in the Class...

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Conference on image and signal processing for Remote Sensing XXII

作者： Quesada-Barriuso, Pablo Heras, Dora B. Arguello, Francisco Univ Santiago de Compostela Ctr Singular Invest Tecnoloxias Informac CiTIUS Santiago Spain Univ Santiago de Compostela Dept Elect & Comp Santiago Spain

ISBN: (数字)9781510604131

ISBN: (纸本)9781510604124;9781510604131

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.

关键词： Remote sensing Land cover classification Hyperspectral analysis wavelet transform Feature extraction Morphological profiles Denoising Spectral-Spatial processing

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A bi-directional associative memory based multiple image watermarking on cover video

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MULTIMEDIA TOOLS AND applications 2016年第12期75卷 7211-7256页

作者： Agilandeeswari, L. Ganesan, K. VIT Univ SITE Vellore 632014 Tamil Nadu India VIT Univ TIFAC CORE Automot Infotron Vellore 632014 Tamil Nadu India

Telemedicine is a rapidly developing application of clinical medicine where medical information is transferred through the internet and other networks for the purpose of consulting, and remote medical procedures or examinations. This paper presents a novel neural network inspired watermarking technique, to enhance the authentication of the transmitted sensitive medical images over telemedicine network. In order to examine the medical images, the multiple scan images of same type or different types can be considered as watermarks and the same are trained using Bidirectional Associative Memory (BAM) neural network. The resultant weight matrix is embedded in the less correlated Principal Component Analysis (PCA) components of the wavelet coefficients of the cover video frames using additive embedding type of watermark. At the receiver end, the same BAM neural network is used with the randomly generated target matrix and the extracted weight matrix as input. As a result of this, the input matrix will be obtained. From the input matrix, the physician can extract their own information or scan images using their private or secret key. The proposed watermarking technique is validated with the existing systems in terms of imperceptibility, robustness and watermark capacity using the metrics such as Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Cross Correlation (NCC), Bit Correction Rate (BCR), Detection rate, Receiver Operating Characteristics (ROC) and payload. The performance of the proposed system is evaluated by introducing various notable image processing attacks, geometrical attacks and video processing attacks on watermarked video. The experimental results demonstrate that the proposed technique has good perceptual quality of 50.6292 dB in terms of PSNR value, robustness of about nearly 1.0000 in terms of NCC value and payload of 1000 watermarks.

关键词： Telemedicine Authentication Imperceptibility Robustness Payload Discrete wavelet Transform BAM PSNR SSIM NCC BCR ROC

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p-exponent and p-leaders, Part I: Negative pointwise regularity

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PHYSICA A-STATISTICAL MECHANICS AND ITS applications 2016年 448卷 300-318页

作者： Jaffard, S. Melot, C. Leonarduzzi, R. Wendt, H. Abry, P. Roux, S. G. Torres, M. E. Univ Paris Est Lab Anal & Math Appl CNRS UMR 8050 UPEC Creteil France Aix Marseille Univ CNRS Cent Marseille I2MUMR 7373 F-13453 Marseille France Univ Lyon ENS Lyon Signal Syst & Phys Phys DeptCNRS UMR 5672 Lyon France Univ Toulouse IRIT CNRS UMR 5505 Toulouse France Univ Nacl Entre Rios Consejo Nacl Invest Cient & Tecn Entre Rios Argentina

Multifractal analysis aims to characterize signals, functions, images or fields, via the fluctuations of their local regularity along time or space, hence capturing crucial features of their temporal/spatial dynamics. Multifractal analysis is becoming a standard tool in signal and image processing, and is nowadays widely used in numerous applications of different natures. Its common formulation relies on the measure of local regularity via the Holder exponent, by nature restricted to positive values, and thus to locally bounded functions or signals. It is here proposed to base the quantification of local regularity on p-exponents, a novel local regularity measure potentially taking negative values. First, the theoretical properties of p-exponents are studied in detail. Second, wavelet-based multiscale quantities, the p-leaders, are constructed and shown to permit accurate practical estimation of p-exponents. Exploiting the potential dependence with p, it is also shown how the collection of p-exponent senriches the classification of locally singular behaviors in functions, signals or images. The present contribution is complemented by a companion article developing the p-leader based multifractal formalism associated to p-exponents. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Pointwise regularity p-exponent wavelet p-leaders Negative regularity Singularity classification Multifractal analysis

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Speech signal denoising with wavelet-transforms and the mean opinion score characterizing the filtering quality 13

Speech signal denoising with wavelet-transforms and the mean...

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Conference on Dynamics and Fluctuations in Biomedical Photonics XIII

作者： Yaseen, Alauldeen S. Pavlov, Alexey N. Hramov, Alexander E. Saratov NG Chernyshevskii State Univ Dept Phys Astrakhanskaya Str 83 Saratov 410012 Russia Univ Technol Baghdad Alsinaa Str Baghdad Iraq Saratov State Tech Univ Res & Educ Ctr Nonlinear Dynam Complex Syst Politech Skaya Str 77 Saratov 410054 Russia Saratov NG Chernyshevskii State Univ Fac Nonlinear Proc Astrakhanskaya Str 83 Saratov 410012 Russia Russian Acad Sci Kotelnikov Inst Radioengn & Elect Saratov Branch Zelyonaya Str 38 Saratov 410019 Russia

ISBN: (纸本)9781628419412

Speech signal processing is widely used to reduce noise impact in acquired data. During the last decades, wavelet-based filtering techniques are often applied in communication systems due to their advantages in signal denoising as compared with the Fourier-based methods. In this study we consider applications of a 1-D double density complex wavelet transform (1D-DDCWT) and compare the results with the standard 1-D discrete wavelet-transform (1D-DWT). The performances of the considered techniques are compared using the mean opinion score (MOS) being the primary metric for the quality of the processed signals. A two-dimensional extension of this approach can be used for effective image denoising.

关键词： wavelet-transform denoising thresholding speech signal

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