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Quasi-analytic directional wavelet packets: applications to image processing

Quasi-analytic directional wavelet packets: applications to ...

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International conference on Sampling Theory and applications (SampTA)

作者： Amir Z. Averbuch Valery A. Zheludev Sch. of Computer Science Tel Aviv University Tel Aviv Israel

Recently, a versatile library of quasi-analytic complex-valued wavelet packets (WPs) which originate from splines of arbitrary orders, was designed [1]. The real parts of the quasi-analytic WPs (qWPs) are the regular spline-based orthonormal WPs. The imaginary parts, which are slightly modified Hilbert transforms of the real parts, are the so-called complementary orthonormal WPs, which, unlike the symmetric regular WPs, are antisymmetric. Both regular and complementary WPs are well localized in time domain and their DFT spectra provide a variety of refined splits of the frequency domain. The waveforms can have arbitrary number of vanishing *** products of 1D quasi-analytic WPs (qWPs) provide a diversity of 2D waveforms oriented in multiple directions. The designed computational scheme enables us to get fast and easy implementation of the qWP transforms. The shapes of real and imaginary parts of the qWPs can be regarded as directional cosine waves with different frequencies modulated by localized low-frequency signals. For example, the set of the fourth-level WPs comprises waveforms which are oriented in 314 different directions and are oscillating with 256 different frequencies. Various combinations of qWPs form multiple frames in the 2D signal *** combination of the exceptional properties of the designed qWPs, such as unlimited directionality and oscillating structure of the waveforms, vanishing moments and refined frequency resolution, make them a powerful tool for image processing applications. The algorithms based on the qWPs proved to be competitive with the best existing methods in solving such classical image processing problems as denoising, inpainting and deblurring. The qWP algorithms are especially efficient for capturing edges and fine texture and oscillating patterns even in severely degraded *** to the above properties and next to unlimited diversity of testing waveforms, the qWPs have strong capabilities for extraction characte

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Comparative analysis on image compression techniques for chromosome images

Comparative analysis on image compression techniques for chr...

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International conference on Advances in Biomedical Engineering (ICABME)

作者： M. Mansour H. Mouhadjer A. Alipacha K. Draoui Faculty of Electrical Engineering University USTO-MB

image compression has emerged as a major research area due to the phenomenal growth of applications that generate process and transmit images. image compression can be sequential or progressive. Natural images contain edges, geometry, texture and other discontinuities (details that are oriented in various directions). In this paper, a comparative analysis of four images compression techniques (JPEG, wavelets, Bandelets and Ridgelets) applied to images of chromosomes is proposed. The interest of this study is to measure the sensitivity to the noise of these techniques when dealing with contour and texture of different forms of objects in the image. This synthesis and from results, proves that the studied factors (compression ratio, processing duration, coding error and signal noise ratio) have a characteristic influence on each other. This indicates that the choice of the result converges to an optimal compromise. Promising results are obtained.

关键词： image coding wavelet transforms Transform coding wavelet analysis Discrete cosine transforms Noise

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BAYESIAN ESTIMATION FOR THE MULTIFRACTALITY PARAMETER

BAYESIAN ESTIMATION FOR THE MULTIFRACTALITY PARAMETER

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IEEE International conference on Acoustics, Speech and signal processing

作者： Herwig Wendt Nicolas Dobigeon Jean-Yves Tourneret Patrice Abry University of Toulouse UMR CNRS 5505 INP-ENSEEIHT F-31062 Toulouse France ENS Lyon Physics Dept. UMR CNRS 5672 F-69007 Lyon France

ISBN: (纸本)9781479903573

Multifractal analysis has matured into a widely used signal and image processing tool. Due to the statistical nature of multifractal processes (strongly non-Gaussian and intricate dependence) the accurate estimation of multifractal parameters is very challenging in situations where the sample size is small (notably including a range of biomedical applications) and currently available estimators need to be improved. To overcome such limitations, the present contribution proposes a Bayesian estimation procedure for the multifraetality (or intermittence) parameter. Its originality is threefold: First, the use of wavelet leaders, a recently introduced multiresolution quantity that has been shown to yield significant benefits for multifractal analysis;Second, the construction of a simple yet generic semi-parametric model for the marginals and covariance structure of wavelet leaders for the large class of multiplicative cascade based multifractal processes;Third, the construction of original Bayesian estimators associated with the model and the constraints imposed by multifractal theory. Performance are numerically assessed and illustrated for synthetic multifractal processes for a range of multifractal parameter values. The proposed procedure yields significantly improved estimation performance for small sample sizes.

关键词： multifractal analysis Bayesian estimation wavelet leaders multiplicative cascade processes log-cumulants Bayes methods MULTIFRACTAL ANALYSIS Sample Size wavelets assessment procedure Creativity Leaders

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Colour Volumetric Compression for Realistic View Synthesis applications

Colour Volumetric Compression for Realistic View Synthesis A...

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

作者： N. Anantrasirichai C. Nishan Canagarajah David W. Redmill David R. Bull Department of Electrical & Electronic Engineering University of Bristol Bristol UK

The colour volumetric data which is constructed from a set of multi-view images is capable of providing realistic immersive experience. However it is not widely applicable due to its manifold increase in bandwidth. This paper presents a novel framework to achieve scalable volumetric compression. Based on wavelet transformation, data rearrangement algorithm is proposed to compact volumetric data leading to high efficiency of transformation. The colour data is also rearranged by using the characteristics of human eye sensitivity. Moreover, the pre-processing for adaptive resolution is proposed in this paper. The low resolution overcomes the limitation of the data transmission at low bit rate, whilst the fine resolution improves the synthesised images' quality. The results of our proposed schemes show significant improvement of the compression performance over the traditional 3D coding.

关键词： image coding Bit rate Bandwidth Humans image resolution Data communication Medical services Biomedical equipment Data engineering Manifolds

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image Super Resolution Using Fractional Discrete wavelet Transformand Fractional Fast Fourier Transform

Image Super Resolution Using Fractional Discrete Wavelet Tra...

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International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Anita Ray Choudhury Vijay R. Dahake Department of Electronics and Telecommunication RamaraoAdik Institute of Tehnology Nerul Navi Mumbai Maharashtra

ISBN: (纸本)9781538614433;9781538614426

Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from the observed LR images. In various image processing fields super resolution is playing a very crucial part. The single-frame as well as multi-frame both the methods in SR are widely used in multiple day to day applications and systems from last few years. In this work, we are proposing an image resolution enhancement technique which has its basis on interpolation of the high-frequency sub-band images obtained by fractional discrete wavelet transform (FDWT). HereFDWT is used to decompose an input low-resolution image into different subbands. Then, interpolation between the high-frequency subband images and the input image takes place, followed by combining all these images to generate a new high-resolution image by using inverse FDWT. Directional selectivity which is provided by FDWT actually produces resolution enhancement, where the high-frequency subbands in different directions contribute to the sharpness of the high-frequency details such as edges. The quantitative peak signal-to-noise ratio (PSNR) and visual results show the superiority of the proposed technique over the conventional bicubic interpolation image resolution enhancement techniques.

关键词： Discrete wavelet transforms Interpolation signal resolution Spatial resolution

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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...

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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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Imperceptibility and robustness study in different transform domain for copyright protection in digital image watermarking in hybrid SVD-domain

Imperceptibility and robustness study in different transform...

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International Symposium on Artificial Intelligence and signal processing (AISP)

作者： Saharul Alom Barlaskar Anish Monsley Kirupakaran Naseem Ahmad Kuldeep Singh Yadav Taimoor Khan Rabul Hussain Laskar Department of ECE National Institute of Technology Silchar Assam India

Copyright protection of digital images through watermarking technique is one of the viable approaches for industrial and commercial applications. Sustaining a watermark’s robustness during attack and preserving the image visual quality with watermark capacity is most challenging. In this work, we show the impact of attacks on the image imperceptibility and watermark robustness for a watermarking technique in hybrid transform domain with various wavelets. We observed that (i) the excellent multi-resolution features of wavelet coefficients help to enhance imperceptibility; (ii) the stability property of SVD ensures adequate robustness; (iii) Shuffling the keys have addressed the rising of FPP occurring in the conventional SVD based systems. The performance of the technique has been enumerated through various signal processing attacks. Performance examining has been done in terms of state-of-the-art measures like PSNR, NCC, SSIM, NSR. A subjective investigation of the experimental results validates the performance of the proposed technique in each transform domain.

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Summation invariant and its applications to shape recognition

Summation invariant and its applications to shape recognitio...

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

作者： Wei-Yang Lin N. Boston Yu Hen Hu Department of Electrical and Computer Engineering University of Wisconsin Madison WI USA

A novel summation invariant of curves under transformation group action is proposed. This new invariant is less sensitive to noise than the differential invariant and does not require an analytical expression for the curve as the integral invariant does. We exploit this summation invariant to define a shape descriptor called a semi-local summation invariant and use it as a new feature for shape recognition. Tested on a database of noisy shapes of fish, it was observed that the summation invariant feature exhibited superior discriminating power compared to that of wavelet-based invariant features.

关键词： Application software Noise shaping Marine animals Shape measurement Testing image databases Spatial databases Computer vision Neural networks Equations

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

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

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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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Analysis of image Fusion techniques over Multispectral and Microwave SAR images

Analysis of Image Fusion techniques over Multispectral and M...

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International conference on Communication and signal processing

作者： Vivek Kumar Gupta Amit Neog S.K. Katiyar M.Tech. Geoinformatics and Its Application Maulana Azad National Institute of Technology Bhopal 462051 INDIA M.Tech. Geoinformatics and Its Application Maulana Azad National Institute of Technology Bhopal 462051 INDIA. Geoinformatics and Its Application Div. Civil Engineering DepartmentMaulana Azad National Institute of Technology Bhopal 462051 INDIA

ISBN: (纸本)9781467348652

In remote sensing applications, the increasing availability of space borne sensors gives a motivation for different image fusion algorithms. Remote sensing image fusion aims at integrating the information conveyed by data acquired which cover different portions of the electromagnetic spectrum at different spatial, temporal and spectral resolutions;so that we can get multi-temporal, multi-resolution and multi-frequency image data for purposes of feature extraction, modeling and classification. The composite or fused image is more useful for human perception as well as for automatic computer analysis task such as feature extraction, segmentation and object recognition. In India, Indian Space Research Organization has recently launched RISAT-1 having microwave imaging sensor and we have used microwave SAR data in our research work for image fusion technique analysis. We have analyzed the Intensity-Hue-Saturation (IHS) transformation, principal component analysis (PCA) and wavelet transform (WT) based fusion techniques considering visual and statistical parameters.

关键词： Synthetic Aperture Radar (SAR) image image Fusion Techniques Kappa Statistics image registration image Classification Accuracy

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