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International conference on Computational Systems and Communications (ICCSC)

作者： A Varsha Preetha Basu Dept. of Electronics and Communication Engineering TKM College of Engineering Kollam Kerala

Noise suppression is an integral part of any image processing task Noise significantly degrades the image quality and hence makes it difficult for the observer to discriminate fine detail of the images especially in diagnostic examinations. Through decades of research, mass articles on image denoising have been proposed The effect of noise in the images can be reduced by using either spatial filtering or transform domain filtering. In transform domain wavelet method provide better denoising while preserving the details of images like edges. The Discrete wavelet Transform (DWT) has some disadvantages that undetermined its application in image processing as lack of shift invariance and poor directional selectivity. In order to overcome these disadvantages Dual Tree Complex wavelet Transform (DT-CWT) is used which provide perfect reconstruction over the traditional wavelet transform It employs 2 real DWTs; the first DWT gives the real part of the transform while second DWT gives the imaginary part. It is nearly shift invariant and directionally selective in two and higher dimensions with limited redundancy. The DTCWT outperforms the DWT for applications like image denoising and enhancement One of the advantages of the DTCWT is that it can be used to implement 2D wavelet transforms that are more selective with respect to orientation than is the 2D DWT. The 2D DTCWT produces twelve sub bands at each scale, each of which are strongly oriented at distinct angles. A Dual Tree Complex wavelet transform based image denoising is proposed which uses generalized cross validation technique The denoising performance for different images using Discrete wavelet Transform and Dual Tree Complex wavelet transform with different thresholding need to be evaluated Evaluation is carried out in terms of various parameters such as Peak signal to Noise Ratio, mean Structural Similarity and Coefficient of Correlation.

关键词： Discrete wavelet transforms Noise Noise reduction image denoising Standards

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Fast identification of JPEG 2000 images for digital cinema profiles

Fast identification of JPEG 2000 images for digital cinema p...

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

作者： Osamu Watanabe Takahiro Fukuhara Hitoshi Kiya Department of Electronics & Computer Systems Takushoku University Hachioji Tokyo Japan CPDG PSG Sony Corporation Atsugi Kanagawa Japan Department of Information and Communication Systems Engineering Tokyo Metropolitan University Hino Tokyo Japan

ISBN: (纸本)9781457705380

A method for identifying JPEG 2000 images with different coding parameters, such as DWT-filters, code-block sizes, quantization step sizes, and resolution levels, is presented. The proposed method does not produce false-negative matches regardless of the different coding parameters. Moreover, the proposed method is fast because it uses the number of zero-bit-planes. This number can be extracted from the JPEG 2000 codestream by only parsing the header information. Experimental results showed the effectiveness of the proposed method for digital cinema applications.

关键词： Transform coding Discrete wavelet transforms image coding Databases Motion pictures image resolution Decoding

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High capacity watermarking using balanced multiwavelet transforms

High capacity watermarking using balanced multiwavelet trans...

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IEEE International conference on image processing

作者： L. Ghouti A. Bouridane S. Boussakta Department of Information and Computer Science King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia School of Computer Science Queen''s University Belfast UK School of Electronic and Electrical Engineering University of Leeds UK

The emergence of digital multimedia and the proliferation of its use have raised major concerns about the protection of intellectual property. In response to these concerns, digital watermarks have emerged as a possible solution for protecting the intellectual property of digital content. In this paper, we derive estimates of data-hiding capacity of balanced multiwavelet transforms. This class of transforms, relatively new, has useful properties for image processing applications as shown in this paper. Furthermore, we investigate the relevance of two closely related statistical models, developed for scalar wavelets, for modeling the statistics of balanced multiwavelet transform coefficients. Finally, we present performance results of a spread spectrum watermarking system that is based on this new transform.

关键词： Watermarking Protection Intellectual property image processing wavelet transforms Spread spectrum communication image coding signal processing Computer science Statistical distributions

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SAR image Despeckling Using Local Contextual Hidden Markov Model in the Contourlet Domain

SAR Image Despeckling Using Local Contextual Hidden Markov M...

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IEEE International conference on Radar

作者： Shuang Wang Xiao Xu Biao Hou Li Cheng Jiao Institute of Intelligent Information Processing and Key Laboratory for Radar Signal Processing Xidian University Xi'an China

Synthetic aperture radar (SAR) image despeckling is an important problem in the SAR applications. A novel despeckling approach using a local contextual hidden Markov model (LCHMM) in the contourlet domain is presented in this paper. The proposed method can not only use the multiresolution and multidirection characteristics of the contourlet transform, but also exploit the local statistics and capture the intrascale dependencies of the contourlet coefficients by using LCHMM. The experiments in despeckling SAR images show that the proposed method in contrary to other methods can obtain a better trade-off between smoothing the homogeneous areas and keeping the edges and can get better visual effect

关键词： Hidden Markov models Filter bank Synthetic aperture radar signal resolution Multiresolution analysis wavelet transforms Radar signal processing Statistics Smoothing methods Context modeling

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Momentum Study Based on CRITIC Evaluation Method and Machine Learning

Momentum Study Based on CRITIC Evaluation Method and Machine...

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image processing and Computer applications (ICIPCA), IEEE International conference on

作者： Jinghua Zhou Jingruo Zhang Beijing Jiaotong University Beijing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper mainly focuses on the evaluation and prediction of sports competition results, and uses the methods of feature extraction, CRITIC weighted analysis and machine learning algorithm to discuss and analyze. First, after data preprocessing, the dimension of column data is reduced by feature extraction and the evaluation index is obtained. These metrics are then weighted using the CRITIC method to obtain a measure of quantified performance. Secondly, the correlation analysis model is introduced to prove the existence of momentum. Finally, by comparing the performance of XGBoost, Extra Trees and Random Forest algorithm in training and prediction, it is found that XGBoost algorithm has the best performance in AUC of test set, reaching 95.05%. The XGBoost parameters were further optimized by Bayesian optimization, and the signal was reconstructed by combining wavelet analysis to improve the accuracy and robustness of the model.

关键词： Analytical models Machine learning algorithms Correlation Games Feature extraction wavelet analysis Prediction algorithms Robustness Random forests Sports

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image Based Detection of Cotton Plant Diseases Using Discrete wavelet Transform Features

Image Based Detection of Cotton Plant Diseases Using Discret...

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IEEE International conference on Intelligent Techniques in Control, Optimization and signal processing (INCOS)

作者： Shalini Mohan Lakshmanan Muthulakshmi Devaraj D Department of Biotechnology Kalasalingam Academy of Research and Education Srivilliputtur India Department of Electrical and Electronics Engineering Kalasalingam Academy of Research and Education Srivillliputur India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Cotton is one of the most vital cash crops cultivated around the globe and its yield directly influences the economy and the livelihood of huge number of people associated with it. The major loss of cotton occurs due to damage incurred by microbial pathogens that infect the cotton crop. The course of infection and their effect on the growth and yield of the plant varies depending upon the type of disease. Appropriate treatment for the specific type of disease could prevent the spread of infection and also reduce the use of pesticide that is usually done during the empirical treatment techniques. Identification of the type of cotton plant disease is critical and needs to be done at a faster pace. image processingbased machine learning approach is found to be a better option for this application. This article deals with development of the machine learning model capable of identifying the different types of cotton plant diseases using image processing. Foliar images of diseased cotton plants are collected from public databases and preprocessing operations like resizing, filtering and contrast enhancement are performed followed by k-means clustering for segmentation of diseased part of the leaf. image decomposition is performed using discrete wavelet transform with Daubechies wavelet at five levels and the mean, standard deviation, and entropy of the coefficients are used for building the machine learning model. Classification with Artificial Neural Network trained with backpropagation algorithm has a classification accuracy of 97% indicating the veracity of the model. Hence, the developed model could be used as for developing a user-friendly interface that could be tapped for real-time applications.

关键词： Plant diseases image segmentation Crops Machine learning Backpropagation algorithms Artificial neural networks Discrete wavelet transforms

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Multiresolution estimation of fractal dimension from images affected by signal-dependent noise

Proceedings of SPIE - The International Society for Optical ...

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3813卷 251-262页

作者： Aiazzi, Bruno Alparone, Luciano Baronti, Stefano Garzelli, Andrea

A well-suited approach to calculate the fractal dimension of digital images stems from the power spectrum of a fractional Brownian motion: the ratio between powers at different scales is related to the persistence parameter H and, thus, to the fractal dimension D = 3 - H. The signal-dependent nature of the speckle noise, however, prevents from a correct estimation of fractal dimension from Synthetic Aperture Radar (SAR) images. Here, we propose and assess a novel method to obtain D based on the multiscale decomposition provided by the normalized Laplacian pyramid (NLP), which is a bandpass representation obtained by dividing the layers of an LP by its expanded baseband and is designed to force the noise to become signal-independent. Extensive experiments on synthetic fractal textures, both noise-free and noisy, corroborate the underlying assumptions and show the performances, in terms of both accuracy and confidence of estimation, of pyramid methods compared with the well-established method based on the wavelet transform. Preliminary results on true SAR images from ERS-1 look promising as well.

关键词： Synthetic aperture radar

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OPTICAL wavelet-BASED signal processing

OPTICAL WAVELET-BASED SIGNAL PROCESSING

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IJCNN International Joint conference on Neural Networks

作者： Yao Li NEC Research Inatliute

After a successful application to the analysis of seismic data in 1984,the geophysicist J. Morlet[1]formalized the concept of wavelets by generalizing similar mathematical and physical decomposition concepts from the previous works of Harr(1910),Gabor(1946),Calderon (1964),etc..Closely related to the concept of pyramids,the key component of the wavelet theory is a set of look-alike functions called wavelets which can form an efficient and powerful decomposition *** square-integrable signal can be selected as the so-called mother wavelet which,in turn,generates a family of daughter wavelets by *** unique feature of the wavelet processing is that not only a multi-resolution signal decomposition can be achieved through a set of linear correlation operations between the input signal and the wavelets,but also the original signal can be reconstructed from the decomposed signals through their convolutions with the same set of *** other interesting feature of the wavelet processing is its capability of forming an orthonomal decomposition basis allowing for an efficient sampling, which could not be achieved by otheT existing windowed Fourier transforms,such as the Gabor *** addition to a successful application to the seismic and geological data,the usefulness of the wavelets to the fractal,turbulence,speech and vision analysis as well as to other industrial and military signal and image processing applications has also been recognized recently. Since a complete wavelet process consists of a direct(DWT) and an inverse(IWT) wavelet transforms characterized by a set of parallel linear correlation and convolution operations,extensive computations are ***,the coordinate expansion and reduction required by the transforms may place additional computation complexity to digital ***-space optics which is inherent to massively parallel linear operations is expected to have solutions to these problems[2,3].Research on optica

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Novel signal processing Techniques for High Angular Resolution Diffusion-Weighted Imaging

Novel Signal Processing Techniques for High Angular Resoluti...

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International conference on Computing and Networking Technology (ICCNT)

作者： Dushyanth V Babu R Praveen Kumar Thakur Shiv Shankar Shankar Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India ARKA JAIN University Jamshedpur Jharkhand India Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Excessive angular decision diffusion-weighted imaging (HARDWI) is an effective approach for visualizing tissue microstructures, which are otherwise hard to look at the usage of conventional MRI technology. To attain this, HARDWI relies on sign-processing techniques to reconstruct images from tremendously anisotropic diffusion-weighted alerts. Novel signal processing strategies advanced in current years have further advanced the abilities of HARDWI. These strategies leverage the sparsity of diffusion signal in more than one angular guideline, allowing the reconstruction of man or woman diffusion occasions within the specimen’s microstructure. wavelet rework-primarily based strategies allow for the advanced decision of the reconstructed images, extended evaluation, and stepped forward signal-to-noise ratio in the resulting photographs. Other strategies, inclusive of diffusion orientation transform, have been advanced to estimate the orientation of fiber tracts better. Furthermore, spatially-variable regularization techniques have been advanced for advanced estimation of the diffusion parameters at a voxel stage. The combination of these signal processing techniques has advanced the application of HARDWI to a spread of biomedical applications, such as neuroanatomy, tumor imaging, and small animal studies.

关键词： wavelet transforms Visualization image resolution Imaging Optical fiber networks Microstructure image reconstruction signal resolution Tumors signal to noise ratio

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Comparative study of different spatial/spatial-frequency methods (Gabor filters, wavelets, wavelets packets) for texture segmentation/classification

Comparative study of different spatial/spatial-frequency met...

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IEEE International conference on image processing

作者： P. Vautrot N. Bonnet M. Herbin Université de Reims Champagne-Ardenne Reims France Unité Inserm 314 Université de Reims Champagne-Ardenne Reims France

The interest manifested into texture recognition has had a substantial growth since these past few years. Much research is focused on a joint space/spatial-frequency representation of images. This paper describes the comparison between different spatial/spatial-frequency methods involving Gabor filters and wavelets. Two applications are considered: image segmentation and texture classification/recognition. It has been seen that results can depend significantly on the method used to decompose the signal. Two aspects are mainly taken into account: the effect of an over-sampling of the frequency domain by a bank of filters, and the ability of 1D wavelets to segment/classify oriented textures. An improvement using angular filters with separable wavelets is demonstrated, while no noticeable amelioration is observed using Battle-Lemarie wavelets, as compared to Gabor filters.

关键词： Gabor filters wavelet packets Feature extraction image segmentation Sampling methods image recognition Filtering Frequency domain analysis Filter bank wavelet domain

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